Electric vehicle fast charging demand guiding method, device, equipment and medium

Abstract

The invention relates to the technical field of electric vehicle charging guiding, in particular to an electric vehicle fast charging demand guiding method, device and equipment and a medium, and the method comprises the steps: building a dynamic traffic network with a real-time updating function; constructing a road traffic impedance model and an electric vehicle power consumption model; respectively constructing a private car travel behavior model and an online car-hailing travel behavior model; constructing a user charging satisfaction quantitative index; putting forward charging selection models of different types of electric vehicles; the charging load of the charging station in each time period is predicted, and the charging station dynamic pricing strategy takes the minimum load fluctuation of each charging station as a target; according to travel modes of different types of electric vehicles such as private vehicles and online-hailed vehicles, spatial and temporal distribution of fast charging loads is optimized, and a strategy can guide a user to charge when the load is relatively low through price regulation and control, so that the load of each charging station is balanced, the power fluctuation pressure caused by fast charging of a power distribution network is reduced, and stable operation of a power grid is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging guidance, and in particular to a method, device, equipment and medium for guiding electric vehicle fast charging demand. Background Art

[0002] Charging stations for electric vehicles often have two charging modes: fast charging and slow charging. Compared with slow charging, fast charging has the characteristic of generating huge power demand in a short period of time, which can easily bring great negative impact to the distribution network. Therefore, various means are used to guide users to evenly disperse to various charging stations for fast charging when their electric vehicles are low on power, thereby changing the temporal and spatial distribution of fast charging loads, which is of great significance to maintaining the voltage stability of the distribution network; charging guidance for electric vehicles is mainly to reduce the congestion in charging stations and improve the utilization of charging facilities.

[0003] However, the existing electric vehicle charging guidance mainly influences users' charging decisions by adjusting electricity prices, usually by lowering prices to attract users to charge during specific time periods. However, the existing charging guidance mainly attracts users to charge during specific time periods by adjusting electricity prices, but this method has obvious limitations in actual operation. Simple price adjustment cannot fully consider the specific travel needs and behavioral characteristics of electric vehicle users, and it is difficult to accurately guide users to disperse charging loads. Therefore, the existing charging guidance measures have limited effects in improving the utilization rate of charging facilities and alleviating congestion. It is urgent to conduct more detailed behavioral analysis and charging satisfaction quantification for different user groups, and optimize them in combination with appropriate pricing strategies.

[0004] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0005] The present invention provides a method, device, equipment and medium for guiding the fast charging demand of an electric vehicle, thereby effectively solving the problems in the background technology.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is: a method for guiding electric vehicle fast charging demand, comprising the following steps:

[0007] S10: Obtain road, traffic information and charging station related information to establish a dynamic traffic network with real-time updates;

[0008] S20: Based on the dynamic traffic network, considering the road travel time and road travel speed of electric vehicles under different traffic conditions, a road traffic impedance model and an electric vehicle power consumption model are constructed;

[0009] S30: Based on the road traffic impedance model and the electric vehicle power consumption model, simulate the travel behavior of the electric vehicle, classify the electric vehicles into types according to different travel patterns of the electric vehicles, including private cars and online-hailing cars, and respectively construct a private car travel behavior model and an online-hailing car travel behavior model;

[0010] S40: Based on the private car travel behavior model and the online car-hailing travel behavior model, a quantitative index of user charging satisfaction is constructed by comprehensively considering travel time, charging electricity price and electricity consumption;

[0011] S50: Based on the quantitative index of user charging satisfaction and the queuing system of charging stations, a charging selection model for different types of electric vehicles is proposed;

[0012] S60: Based on the charging selection model and the queuing system of the charging station, the charging load of the charging station in each time period is predicted, and a dynamic pricing strategy for the charging station is formulated with the goal of minimizing the load fluctuation of each charging station.

[0013] Furthermore, in step S20, the road traffic impedance model includes:

[0014]

[0015] In the formula, R l is the total travel time of section l; T 0,l is the travel time when the road section l is unobstructed; Q1 is the traffic path of the road section l; C l is the actual traffic capacity of section l; L l is the length of section l; N l is the number of roadside vehicles on section l; τ is the influence coefficient of roadside vehicles on the traffic of the section.

[0016] Furthermore, in step S20, the electric vehicle power consumption model includes:

[0017]

[0018] In the formula, E lose,l is the power consumption of the electric vehicle on section l; V l is the average speed of the electric vehicle on road section l. The speeds corresponding to different levels of roads and different road conditions are different; L l is the length of section l; a, b and c are power consumption coefficients.

[0019] Further, in step S30, according to different travel patterns of electric vehicles, electric vehicle types are divided into private cars and online-hailing cars, and a private car travel behavior model and an online-hailing car travel behavior model are constructed respectively, including:

[0020] S31: Determine the travel behavior of private cars, including using trip chains with residential areas as the starting and ending points to describe the travel behavior of private cars. The first trip time and stay time of each type of trip chain follow a normal distribution;

[0021] S32: Determine the travel behavior of the online-hailing vehicle, including describing the travel behavior of the online-hailing vehicle using a travel state transfer matrix, and the first travel time and the number of travels of the online-hailing vehicle follow a normal distribution;

[0022] S33: Based on the travel behavior of the private car and the travel behavior of the online car-hailing vehicle, respectively planning the routes of the private car and the online car-hailing vehicle, including:

[0023] When traveling, private cars plan their routes with the goal of searching for the shortest road travel time;

[0024] When the online ride-hailing service is carrying passengers, the route is planned with the goal of minimizing the distance traveled by the electric vehicle. When the online ride-hailing service is empty, the route is planned with the goal of minimizing the energy consumption or road travel time of the electric vehicle.

[0025] S34: Based on the planning of the routes of the private car and the online-hailing car, the arrival time and remaining power of the electric car are calculated.

[0026] Further, in step S34, the arrival time and remaining power of the electric vehicle are calculated, and the model includes:

[0027]

[0028] In the formula, is the time when the hth electric car arrives at the activity location p; is the time when the hth electric car leaves the activity location p-1; is the travel time from node i to node j; Ω p-1,p is the set of road segments set in the path from activity location p-1 to activity location p; is the remaining power of the hth electric vehicle when it arrives at the activity location p; is the remaining power of the hth electric vehicle when it leaves the activity location p-1; E lose，i,j is the power consumption of the road section from node i to node j.

[0029] Furthermore, in step S40, the travel time, charging electricity price and power consumption are comprehensively considered to construct a quantitative index of user charging satisfaction, including:

[0030] f CSR,k =αf 1,k +βf 2,k +γf 3,k ;

[0031] In the formula, f CSR,k The comprehensive charging satisfaction of electric vehicles charging at charging station k; f 1,k is the charging travel time satisfaction of electric vehicles arriving at charging station k; f 2,k is the charging travel time satisfaction of electric vehicles arriving at charging station k; f 3,k is the satisfaction of the electric vehicle with the charging electricity price when it arrives at the charging station k; α, β and γ are the weight coefficients of the user's charging satisfaction index, and their sum is 1.

[0032] Furthermore, in step S50, based on the user charging satisfaction quantitative index and the queuing system of the charging station, a charging selection model for different types of electric vehicles is proposed, including:

[0033] S51: Determine the charging demand of private cars, including before each trip, when the remaining power of the electric car is not enough to support the next destination, the electric car user will choose to detour to the charging station for charging; if fast charging is required during the trip between two stopovers, the private car will detour to the charging station for charging;

[0034] S52: Determine the charging demand of online-hailing vehicles, including that when the remaining battery power of an online-hailing vehicle is lower than a certain indicator and the vehicle is in an unloaded state, it will go to a charging station to charge before carrying passengers;

[0035] S53: According to the charging demand judgment results of the private car and the charging demand judgment results of the online car-hailing vehicle, the most suitable charging station is selected based on the user charging satisfaction quantitative index, and the charging time and load demand are calculated according to the initial power and charging station conditions;

[0036] S54: According to the charging time and load demand, the queuing system of the charging station determines the queuing and charging order of the electric vehicles based on a first-come, first-served principle.

[0037] Furthermore, in step S60, based on the charging selection model and the queuing system of the charging station, the charging load of the charging station in each time period is predicted, and a dynamic pricing strategy for the charging station with the goal of minimizing the load fluctuation of each charging station is formulated, including:

[0038] S61: construct a charging station load prediction model to obtain a predicted charging load of the charging station;

[0039] S62: According to the maximum charging load capacity that different charging stations can accommodate, the predicted load of each charging station is converted to the maximum capacity, and the average predicted load of the charging station is calculated;

[0040] S63: When the difference between the predicted charging load of each charging station and the average predicted load is less than or greater than the adjustment threshold, adjusting the electricity price of the charging station;

[0041] S64: According to the adjustment of the charging station electricity price, the charging electricity price adjustment step is optimized based on the particle swarm optimization algorithm with a compression factor.

[0042] Furthermore, in step S62, the average predicted load of the charging station is calculated, and the model includes:

[0043]

[0044]

[0045] In the formula, is the average predicted load demand during period t; is the predicted load demand of charging station k in period t; k is the calculation factor of charging station k. When the charging load capacity of charging station k is equal to the maximum capacity of all charging stations, then σ k =1; K is the total number of charging stations; q k is the number of charging piles in charging station k.

[0046] Further, in step S63, when the difference between the predicted charging load demand of each charging station and the average predicted load is less than or greater than the adjustment threshold, the electricity price of the charging station is adjusted, and the model includes:

[0047]

[0048] In the formula, C k,t is the charging electricity price of charging station k in period t; Δc k is the charging price adjustment step; ε is the dead zone coefficient; is the average predicted load demand during period t; is the predicted load demand of charging station k in period t; k is the calculation factor of charging station k. When the charging load capacity of charging station k is equal to the maximum capacity of all charging stations, then σ k =1.

[0049] The present invention also includes a fast charging demand guidance device for electric vehicle users' charging willingness, using the above method, including:

[0050] A dynamic traffic network establishment unit is used to obtain road, traffic information and charging station related information to establish a dynamic traffic network with real-time updates;

[0051] An impedance and power consumption model building unit is used to build a road traffic impedance model and an electric vehicle power consumption model based on the dynamic traffic network and taking into account the road travel time and road travel speed of electric vehicles under different traffic conditions;

[0052] A travel behavior model building unit is used to simulate the travel behavior of electric vehicles based on the road traffic impedance model and the electric vehicle power consumption model, and classify electric vehicles into types according to different travel patterns of electric vehicles, including private cars and online ride-hailing vehicles, and respectively build a private car travel behavior model and an online ride-hailing vehicle travel behavior model;

[0053] A user satisfaction building unit, for building a quantitative index of user charging satisfaction based on the private car travel behavior model and the online car-hailing travel behavior model, taking into account travel time, charging electricity price and electricity consumption;

[0054] A charging selection model building unit, used to propose charging selection models for different types of electric vehicles based on the user charging satisfaction quantitative index and the queuing system of the charging station;

[0055] A dynamic pricing strategy unit is used to predict the charging load of the charging station in each time period based on the charging selection model and the queuing system of the charging station, and to formulate a dynamic pricing strategy for the charging station with the goal of minimizing the load fluctuation of each charging station.

[0056] The present invention also includes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described above is implemented.

[0057] The present invention also includes a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0058] The beneficial effects of the present invention are:

[0059] The present invention accurately depicts the travel and charging behaviors of various types of electric vehicles by comprehensively considering the real-time changes in traffic flow and the travel behavior patterns of different types of electric vehicles; according to the actual needs of different users, a quantitative indicator of user charging satisfaction is established, and combined with appropriate charging pricing strategies, electric vehicles are reasonably guided to fast charge; for the travel patterns of different types of electric vehicles such as private cars and online car-hailing vehicles, the system can cope with the charging load peak problems that may occur at specific times and places, and optimize the spatiotemporal distribution of fast charging loads; in this way, the pressure on the distribution network during charging peaks is effectively reduced, and the stability of the grid voltage is maintained; at the same time, the present invention lays an important technical foundation for further research on comprehensive charging guidance strategies that take into account both slow charging and fast charging of electric vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] 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 some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0061] Figure 1 A flow chart of a method for guiding the fast charging demand of electric vehicles;

[0062] Figure 2 A flow chart of a method for guiding the demand for fast charging of electric vehicles;

[0063] Figure 3 Flowchart for forecasting charging demand for private cars;

[0064] Figure 4 Flowchart for predicting charging demand for online ride-hailing vehicles

[0065] Figure 5 The dynamic pricing flow chart of charging stations;

[0066] Figure 6 A schematic diagram of the structure of a fast charging demand guidance device for electric vehicle users' charging willingness;

[0067] Figure 7 A schematic diagram of the structure of a computer device. DETAILED DESCRIPTION

[0068] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0069] like Figures 1 to 5 As shown: A method for guiding electric vehicle fast charging demand, comprising the following steps:

[0070] S10: Obtain road, traffic information and charging station related information to establish a dynamic traffic network with real-time updates;

[0071] S20: Based on the dynamic traffic network, considering the road travel time and road travel speed of electric vehicles under different traffic conditions, a road traffic impedance model and an electric vehicle power consumption model are constructed;

[0072] S30: Based on the road traffic impedance model and the electric vehicle power consumption model, the travel behavior of electric vehicles is simulated. According to the different travel patterns of electric vehicles, electric vehicles are divided into types, including private cars and online ride-hailing vehicles, and private car travel behavior models and online ride-hailing vehicle travel behavior models are constructed respectively;

[0073] S40: Based on the private car travel behavior model and the online car-hailing travel behavior model, the user charging satisfaction quantitative index is constructed by comprehensively considering the travel time, charging electricity price and electricity consumption;

[0074] S50: Based on the quantitative indicators of user charging satisfaction and the queuing system of charging stations, a charging selection model for different types of electric vehicles is proposed;

[0075] S60: Based on the charging selection model and the queuing system of charging stations, the charging load of charging stations in each time period is predicted, and a dynamic pricing strategy for charging stations is formulated with the goal of minimizing the load fluctuation of each charging station.

[0076] The present invention accurately depicts the travel and charging behaviors of various types of electric vehicles by comprehensively considering the real-time changes in traffic flow and the travel behavior patterns of different types of electric vehicles; according to the actual needs of different users, a quantitative indicator of user charging satisfaction is established, and combined with appropriate charging pricing strategies, electric vehicles are reasonably guided to fast charge; for the travel patterns of different types of electric vehicles such as private cars and online car-hailing vehicles, the system can cope with the charging load peak problems that may occur at specific times and places, and optimize the spatiotemporal distribution of fast charging loads; in this way, the pressure on the distribution network during charging peaks is effectively reduced, and the stability of the grid voltage is maintained; at the same time, the present invention lays an important technical foundation for further research on comprehensive charging guidance strategies that take into account both slow charging and fast charging of electric vehicles.

[0077] By constructing a road traffic impedance model and an electric vehicle power consumption model, and taking into account the travel time and power consumption of electric vehicles under different traffic conditions, the charging guidance strategy can more accurately predict the remaining power and range of the vehicle, thereby helping users plan the charging time and location more scientifically.

[0078] By dividing electric vehicles into different types, such as private cars and online-hailing taxis, and constructing travel behavior models for each type, we can provide personalized guidance for the travel needs and charging behaviors of different types of electric vehicles. For different types of electric vehicle users, we can provide more personalized charging guidance strategies to ensure that the guidance measures can adapt to the charging habits and travel needs of different users, making up for the lack of consideration of differentiated user needs in existing methods.

[0079] By constructing a quantitative indicator of user charging satisfaction, which comprehensively considers multiple factors such as travel time, charging electricity price and electricity consumption, the user's overall charging experience can be significantly improved. Users no longer choose charging stations based on a single factor (such as price), but make intelligent decisions based on comprehensive satisfaction, which improves the comfort and satisfaction during the charging process.

[0080] Based on user charging satisfaction and charging station queuing system, a dynamic charging station selection model is proposed to guide electric vehicle users to choose suitable charging stations according to actual needs, avoid overcrowding of some charging stations, optimize the allocation of charging resources, reduce excessive congestion at charging stations during peak hours, optimize the utilization rate of charging facilities, enable more users to complete charging more efficiently, and solve the problem of uneven resource allocation due to the crowding of users in existing charging stations.

[0081] Based on the charging selection model and the queuing system of charging stations, combined with the prediction of the charging station load in each time period, a dynamic pricing strategy with the goal of minimizing the load fluctuation of the charging station is proposed. This strategy can guide users to charge when the load is lower through price regulation, thereby balancing the load of each charging station, reducing the power fluctuation pressure on the distribution network caused by fast charging, and ensuring the stable operation of the power grid.

[0082] As a preference of the above embodiment, in step S20, the road traffic impedance model includes:

[0083]

[0084] In the formula, R l is the total travel time of section l; T 0,l is the travel time when the road section l is unobstructed; is the traffic load of section l; Q1 is the traffic path of section l; C l is the actual traffic capacity of section l; L l is the length of section l; N l is the number of roadside vehicles on section l; τ is the influence coefficient of roadside vehicles on the traffic of the section.

[0085] This model not only takes into account the travel time under normal traffic conditions, but also combines actual conditions such as traffic load and road length, and can dynamically reflect the road resistance under different traffic conditions; by using traffic load and roadside vehicle impact coefficient τ, this scheme can well adapt to the travel needs of electric vehicles under different complex road conditions, especially on congested or narrow roads, and can provide a more realistic time estimate.

[0086] In this embodiment, in step S20, the electric vehicle power consumption model includes:

[0087]

[0088] In the formula, E lose,l is the power consumption of electric vehicles on road section l; V l is the average speed of the electric vehicle on road section l. The speeds corresponding to different levels of roads and different road conditions are different; L l is the length of section l; a, b and c are power consumption coefficients.

[0089] The changes in driving speed caused by different road grades and road conditions are taken into account, allowing the model to flexibly adapt to various actual road conditions and be closer to the energy consumption performance of electric vehicles in real environments. Accurate energy consumption calculations provide electric vehicle users with more comprehensive energy consumption estimates when choosing different routes, helping users choose the most energy-efficient driving route or charging station based on the current power level.

[0090] As a preferred embodiment of the above embodiment, in step S30, electric vehicles are divided into types according to different travel patterns of electric vehicles, including private cars and online-hailing cars, and private car travel behavior models and online-hailing car travel behavior models are constructed respectively, including:

[0091] S31: Determine the travel behavior of private cars, including using trip chains with residential areas as the starting and ending points to describe the travel behavior of private cars. The first trip time and stay time of each type of trip chain follow a normal distribution;

[0092] S32: Determine the travel behavior of the online car-hailing vehicle, including using the travel state transfer matrix to describe the travel behavior of the online car-hailing vehicle. The first travel time and the number of trips of the online car-hailing vehicle follow a normal distribution; the travel state transfer matrix can be expressed as:

[0093]

[0094] In the formula, p t is the state transfer matrix of electric vehicles in each period of 24 hours a day; p mn (m,n=1,2,3) is the transfer probability of electric vehicles between various functional areas; R, W and O represent other areas such as residential areas, work areas and commercial areas respectively;

[0095] S33: Based on the travel behaviors of private cars and online ride-hailing vehicles, the routes of private cars and online ride-hailing vehicles are planned respectively, including:

[0096] When traveling, private cars plan their routes with the goal of searching for the shortest road travel time;

[0097] When the online ride-hailing service is carrying passengers, the route is planned with the goal of minimizing the distance traveled by the electric vehicle. When the online ride-hailing service is empty, the route is planned with the goal of minimizing the energy consumption or road travel time of the electric vehicle.

[0098] S34: Based on the route planning of private cars and online-hailing taxis, the arrival time and remaining power of electric vehicles are calculated.

[0099] There are significant differences in travel patterns between private cars and online ride-hailing vehicles. After constructing models separately, their behavioral characteristics can be more accurately characterized. Private cars are mainly based on fixed travel chains, while online ride-hailing vehicles involve more random factors. The state transfer matrix can be used to better simulate the complex operating rules of online ride-hailing vehicles; personalized behavioral modeling helps to provide more accurate charging guidance for different user groups, avoids the single model's neglect of user needs, and improves the flexibility and accuracy of charging guidance.

[0100] In step S34, the arrival time and remaining power of the electric vehicle are calculated, and the model includes:

[0101]

[0102] In the formula, is the time when the hth electric car arrives at the activity location p; is the time when the hth electric car leaves the activity location p-1; is the travel time from node i to node j; Ω p-1,p is the set of road segments set in the path from activity location p-1 to activity location p; is the remaining power of the hth electric vehicle when it arrives at the activity location p; is the remaining power of the hth electric vehicle when it leaves the activity location p-1; E lose，i,j is the power consumption of the road section from node i to node j.

[0103] The model combines the route planning, travel time and energy consumption of electric vehicles, and can provide high-precision basic data for the charging guidance system, so that the charging guidance system can not only dynamically adjust charging suggestions based on the real-time remaining power and travel time of the electric vehicle, but also provide users with intelligent charging station selection and optimal charging time; through accurate arrival time prediction, the system can better guide users to adjust charging time and reduce time wasted on the way; through precise calculation of energy consumption, users can choose the most suitable charging station and time according to the actual energy consumption of the vehicle, avoid charging in advance or missing charging opportunities, and improve charging efficiency.

[0104] In this embodiment, in step S40, the travel time, charging electricity price and power consumption are comprehensively considered to construct a quantitative index of user charging satisfaction, including:

[0105] S41: Construct the charging travel time satisfaction of electric vehicle users as follows:

[0106]

[0107] In the formula, f 1,k is the charging travel time satisfaction of electric vehicles arriving at charging station k; T kT is the time it takes for an electric vehicle to reach charging station k; min The shortest journey time for an electric vehicle to all alternative charging stations; T max The longest journey time for electric vehicles to all alternative charging stations; T wait,k T is the charging waiting time for the electric vehicle to arrive at the charging station k; waitmax The maximum charging queue time that electric vehicle users can tolerate;

[0108] S42: The charging distance and power consumption satisfaction of electric vehicle users is constructed as follows:

[0109]

[0110] In the formula, f 2,k P is the satisfaction of the electric vehicle’s charging travel time when it arrives at charging station k; lose,k P is the power consumption of the electric vehicle when it arrives at the charging station k; losemin The minimum power consumption for electric vehicles to go to all alternative charging stations; losemax The maximum amount of electricity consumed by electric vehicles when going to all alternative charging stations;

[0111] S43: Construct the charging price satisfaction of electric vehicle users as follows:

[0112]

[0113] In the formula, f 3,k C is the satisfaction of the charging electricity price of the electric vehicle when it arrives at the charging station k; k is the charging electricity price of the electric vehicle arriving at charging station k; C min The minimum charging price for electric vehicles to go to all alternative charging stations; C max The maximum charging price for electric vehicles among all the alternative charging stations;

[0114] S44: The comprehensive charging satisfaction index of electric vehicle users is constructed as follows:

[0115] f CSR,k =αf 1,k +βf 2,k +γf 3,k ;

[0116] In the formula, f CSR,k The comprehensive charging satisfaction of electric vehicles charging at charging station k is used as a quantitative indicator of user charging satisfaction; f 1,k is the charging travel time satisfaction of electric vehicles arriving at charging station k; f 2,k is the charging travel time satisfaction of electric vehicles arriving at charging station k; f 3,kis the satisfaction of the electric vehicle with the charging electricity price when it arrives at the charging station k; α, β and γ are the weight coefficients of the user's charging satisfaction index, and their sum is 1.

[0117] By comprehensively considering the three dimensions of travel time, power consumption and electricity price, a more accurate quantitative model of user charging satisfaction is constructed. This model can be adjusted according to the preferences of different users to make charging guidance more personalized; by balancing the distribution of users in different charging stations, the resource utilization of charging stations is optimized, and the waste or unreasonable allocation of charging station resources is avoided; electricity price satisfaction, as an important indicator for users to choose charging stations, can effectively guide users to charge in time periods or charging stations with lower electricity prices and lighter loads, thereby reducing the pressure on charging stations during peak hours, reducing the load fluctuations of charging stations during peak hours, improving the stability and safety of the power grid and charging stations, balancing the load of charging stations, and optimizing the operation of the charging network.

[0118] As a preferred embodiment of the above embodiment, in step S50, based on the quantitative index of user charging satisfaction and the queuing system of the charging station, a charging selection model for different types of electric vehicles is proposed, including:

[0119] S51: Determine the charging demand of private cars, including before each trip, when the remaining power of the electric car is not enough to support the next destination, the electric car user will choose to detour to the charging station for charging; if fast charging is required during the trip between two stopovers, the private car will detour to the charging station k for charging; the charging demand judgment is as follows:

[0120]

[0121] In the formula, The minimum amount of electricity consumed to reach the nearest charging station to the destination p;

[0122] S52: Determine the charging demand of online-hailing vehicles, including that when the remaining battery power of an online-hailing vehicle is lower than a certain indicator and the vehicle is in an unloaded state, it will go to a charging station to charge before carrying passengers. The charging demand of online-hailing vehicles is determined as follows:

[0123]

[0124] Where, δ is the power coefficient for judging whether the taxi is charged; E 0,h is the battery capacity of the electric vehicle h;

[0125] S53: According to the charging demand judgment results of private cars and online car-hailing vehicles, the most suitable charging station is selected based on the quantitative index of user charging satisfaction, and the charging time and load demand are calculated according to the initial power and charging station conditions; the charging time of electric vehicles can be expressed as:

[0126]

[0127] Where, T C The charging time of electric vehicles; E O is the ideal state of charge for electric vehicles; E t is the initial charging state of the electric vehicle; η C is the charging efficiency; P C is the charging power;

[0128] The charging load of electric vehicles can be expressed as:

[0129]

[0130] Where P k,t is the charging load demand of charging station k in period t; P h,k,t The charging load demand of electric vehicle h at charging station k and time period t; N is the total number of electric vehicles;

[0131] S54: According to the charging time and load demand, the queuing system of the charging station determines the queuing and charging order of electric vehicles based on the first-come-first-served principle.

[0132] According to the relationship between the number of electric vehicles and the number of chargers in the station, there are waiting queues and charging queues. arrive h,k |h∈[1,N k ]} and D={t departure h,k |h∈[1,N k ]}Record the time when electric vehicle h arrives at and leaves fast charging station k in chronological order, where N k is the number of electric vehicles charging at charging station k; the number of vehicles charging at charging station k in each period can be expressed as:

[0133] NEV(k,t)=num(A,t)-num(D,t);

[0134] Where NEV(k,t) is the number of vehicles that need to be charged at charging station k in time period t; num(A,t) and num(D,t) are the cumulative number of electric vehicles that arrive and leave before time t;

[0135] When there are charging piles remaining at the charging station, the charging queue waiting time of the electric vehicle is 0; otherwise, the charging queue waiting time of the electric vehicle is as follows:

[0136]

[0137] In the formula, t w Remaining charging time for electric vehicles; is the number of charging piles at charging station k.

[0138] By judging the charging needs of private cars and online ride-hailing vehicles respectively, the system can provide personalized charging suggestions for different types of users. Private cars focus on driving safety and power supply, while online ride-hailing vehicles focus more on charging time and operational efficiency. The system optimizes the charging guidance strategy according to these needs; personalized charging guidance effectively improves the flexibility of the system, meets the actual needs of different user groups, and reduces the uncertainty in charging decisions.

[0139] like Figure 3 As shown in the figure, the private car charging demand prediction flow chart: the specific steps include:

[0140] Initialization input of raw data: When the system starts, the raw data is first input, including vehicle information, charging status, travel plan, etc. of the private car;

[0141] Set the initial loop parameters: Initialize the loop variable h to 1, indicating that the data of the first private car has been processed;

[0142] Read vehicle battery capacity: Read the battery capacity of the hth private car and obtain its current remaining power;

[0143] Extracting the travel chain type: Extracting the travel chain type of the private car based on the vehicle's historical behavior data or planned itinerary, and recording the first departure time, initial power, and initial departure node;

[0144] Extract the destination node of the travel chain: extract the destination node of this trip from the travel chain;

[0145] Path planning: The system plans the shortest path for private cars from the current starting point to the destination with the goal of minimizing travel time. Based on the path, the system calculates the time and power consumed by the private car from the current starting point to the destination node;

[0146] Check the battery level: Determine the remaining battery level of the private car when it reaches the target node Is it lower than the minimum power required to reach the next target node?

[0147] If yes, the system will plan a detour for the vehicle to the nearest charging station for charging, and select the charging station with the highest user satisfaction (taking into account factors such as charging time, electricity price, and waiting time); after charging is completed, the shortest path will be re-planned to continue to the target node; if no, the system will directly go to the target node along the originally planned path without detouring to the charging station;

[0148] Check whether the trip is finished: After the vehicle reaches the destination node, the system checks whether it has reached the starting node of the trip chain (indicating whether the trip chain is finished);

[0149] If not, update the trip chain status and continue to process the next trip; return to the step to extract the trip chain type, extract the destination of the next trip chain, repeat the above steps, if yes, check whether there are other vehicles (h) data to be processed;

[0150] Process the next private car data: If there are more private car data to be processed, the system will increase h by 1, read the battery capacity of the next car, and repeat the above steps; if the trips of all vehicles have been processed, the process ends.

[0151] like Figure 4 As shown in the figure, the charging demand forecasting flow chart of online car-hailing service is shown in the figure. The specific steps include:

[0152] Input raw data: The system first inputs the raw data of the online car-hailing vehicle, including the vehicle’s battery capacity, travel records, and other information;

[0153] Set loop parameters: Initialize loop variable h to 1, indicating that the data of the first online car-hailing vehicle has been processed;

[0154] Read the battery capacity of online-hailing vehicles: read the battery capacity of the hth online-hailing vehicle and obtain the current remaining power information;

[0155] Extracting the number of trips and initial status: The system extracts the number of trips, the first departure time, the initial battery state SOC (State of Charge), and the initial departure node of the online car-hailing vehicle; the system records the current battery capacity and current location of the vehicle to prepare for subsequent planning;

[0156] Check whether the remaining battery power is below the threshold: Determine the remaining battery power of the online car-hailing service Is the battery level lower than the threshold δE? 0,h (lower limit of battery power);

[0157] If the battery is insufficient, the system will automatically enter charging mode, plan the vehicle to go to a suitable charging station for charging, select the charging station with the highest user satisfaction, and optimize the charging strategy; if the battery is insufficient, the system will continue to plan the next trip;

[0158] Extracting the destination node based on the state transfer matrix: Using the state transfer matrix model, the system extracts the next target destination node of the online car-hailing vehicle. This step is dynamically planned based on the online car-hailing vehicle's operating model and the state transfer matrix.

[0159] Path planning and energy consumption calculation: The system plans the path with the goal of minimizing energy consumption, determines the optimal driving route from the current node to the target node, and calculates the time and power consumption required for the online car-hailing vehicle to reach the target node.

[0160] Check whether the number of trips reaches the preset value: The system determines whether the number of trips of the current online car-hailing service is equal to the preset number of sampling times;

[0161] If not, continue to execute the next travel operation. Return to the step to extract the destination node according to the state transfer matrix, continue to extract the next target node, perform path planning and power consumption calculation; if yes, the current online car-hailing charging demand prediction process ends;

[0162] Process the next online car-hailing data: If there is more online car-hailing data to be processed, the system will increase h by 1, read the battery capacity of the next car, and repeat the above steps; if all vehicle data has been processed, the process ends.

[0163] As a preferred embodiment of the above, in step S60, based on the charging selection model and the queuing system of the charging station, the charging load of the charging station in each time period is predicted, and the dynamic pricing strategy of the charging station with the goal of minimizing the load fluctuation of each charging station includes:

[0164] S61: Construct a charging station load prediction model to obtain a predicted charging load of the charging station; the model includes:

[0165]

[0166] In the formula, is the predicted load demand of charging station k in period t+1; is the new load of charging station k in period t; is the charging load in charging station k during period t; is the load that decides to charge at charging station k during period t; is the canceled charging load of charging station k in the period t+1;

[0167] S62: According to the maximum charging load capacity that different charging stations can accommodate, the predicted load of each charging station is converted to the maximum capacity, and the average predicted load of the charging station is calculated;

[0168] S63: When the difference between the predicted charging load of each charging station and the average predicted load is less than or greater than the adjustment threshold, the electricity price of the charging station is adjusted;

[0169] S64: According to the adjustment of the charging station electricity price, the charging electricity price adjustment step Δc is calculated based on the particle swarm optimization algorithm with compression factor kTo ensure that when the loads of different charging stations vary greatly, the electricity price adjustment can effectively guide users to disperse to charging stations with smaller loads and reduce load fluctuations.

[0170] Through reasonable load forecasting and dynamic pricing control, the system can effectively avoid excessive charging during peak hours, reduce the impact of charging peak hours on the distribution network, ensure the stable operation of the distribution network, reduce the burden on the power grid during peak hours, avoid power grid pressure caused by concentrated fast charging demand, and improve the stability and security of the overall power grid; through load forecasting and price adjustment, the system can reasonably allocate charging station resources, reduce users' queuing and waiting time during peak hours, improve the efficiency of the overall charging process, reduce user waiting time, improve the charging experience, and at the same time improve the overall operation efficiency of the system, so that charging stations can operate in a more efficient environment.

[0171] In step S62, the average predicted load of the charging station is calculated, and the model includes:

[0172]

[0173] In the formula, is the average predicted load demand during period t; is the predicted load demand of charging station k in period t; k is the calculation factor of charging station k. When the charging load capacity of charging station k is equal to the maximum capacity of all charging stations, then σ k =1; K is the total number of charging stations; q k is the number of charging piles in charging station k.

[0174] By predicting the load of charging stations and calculating the average load, the system can identify earlier which charging stations may exceed the safety threshold, so as to make early adjustments. This not only optimizes the load distribution of charging stations, but also avoids the pressure on the distribution network caused by excessive load, ensuring the stable operation of the distribution network; it reduces the load fluctuation pressure caused by fast charging of electric vehicles, protects the operation safety of the power grid, and improves the stability of the overall power supply system; through load prediction and reasonable electricity price adjustment, users can more accurately choose charging stations with lower loads, thereby reducing waiting time. The system can alleviate the congestion of charging stations during peak hours by dynamically adjusting electricity prices and load distribution, thereby improving users' charging efficiency and overall experience; it reduces the time users wait for charging during peak hours, optimizes the charging experience, and improves the service quality of the charging system.

[0175] In this embodiment, in step S63, when the difference between the predicted charging load demand of each charging station and the average predicted load is less than or greater than the adjustment threshold, the electricity price of the charging station is adjusted. The model includes:

[0176]

[0177] In the formula, C k,t is the charging electricity price of charging station k in period t; Δc k is the charging price adjustment step; ε is the dead zone coefficient; is the average predicted load demand during period t; is the predicted load demand of charging station k in period t; k is the calculation factor of charging station k. When the charging load capacity of charging station k is equal to the maximum capacity of all charging stations, then σ k =1.

[0178] Through dynamic electricity price adjustment, the system can effectively guide users to charge during periods of lighter load, reduce the pressure on the power grid during peak periods, ensure the stability of the power grid, balance the charging demand of electric vehicles with the fluctuations in the power grid load, make the charging process smoother and more efficient, reduce the impact of peak charging periods on the power grid, improve the stability of the power grid, and ensure the dynamic balance between the charging demand of electric vehicles and the power grid load; through the electricity price adjustment model based on load forecasting, the system can provide users with real-time and accurate electricity price information to help users make more rational charging choices. Users can choose the most suitable charging station and time period according to the dynamic changes in load and electricity prices, thereby reducing charging costs; it improves the intelligence level of users' charging decisions, helps users reduce charging costs, and improves charging efficiency and user experience.

[0179] like Figure 5 As shown in the flowchart of the dynamic pricing of a charging station of the present invention, the specific steps include:

[0180] Input raw data: The system first inputs the raw data of the charging station, including basic information such as the real-time load, historical data, and current electricity price of each charging station;

[0181] Determine the initial station selection results: Based on the input data, the system preliminarily determines the initial status of each charging station and calculates the predicted load of each charging station in different time periods;

[0182] Obtain the predicted load of each charging station in each time period: Based on the initial station selection results, the system calculates the predicted load value of each charging station in each time period, combines historical data and current electric vehicle charging demand, and generates a load forecast for each time period.

[0183] Based on the predicted load in each period, the average predicted load of the charging station is obtained: the system calculates the load in each period and calculates the average predicted load of each charging station; this step helps standardize the load of charging stations with different capacities so that subsequent electricity price adjustments can be based on a unified standard;

[0184] Determine whether to update the electricity price based on the average predicted load of the charging station: The system compares the current load of the charging station with the average predicted load to determine whether the current load exceeds or falls below the adjustment threshold. If the load fluctuates greatly, the electricity price of the charging station needs to be adjusted;

[0185] The charging price adjustment step length Δck based on intelligent optimization algorithm The system calculates and optimizes the price adjustment step length Δck based on intelligent optimization algorithm (such as particle swarm optimization algorithm) to ensure that the price adjustment range is reasonable and avoid unreasonable user behavior caused by excessive or small adjustments;

[0186] Update the electricity price of each charging station: Based on the optimization results, the system updates the electricity price of each charging station to match the real-time load level, so as to guide users to reasonably disperse their charging needs;

[0187] Cycle judgment: The system judges whether the current cycle has exceeded the predetermined number of cycles, or whether the difference between the previous and subsequent electricity price updates is already very small; if so (such as reaching the optimal electricity price or the electricity price change has become stable), the cycle ends; otherwise, it returns to the step of obtaining the predicted load of each charging station in each period and continues to optimize the electricity price;

[0188] Obtain the optimal electricity price for each charging station: After multiple cycles of optimization, the system obtains the optimal electricity price for each charging station under different load conditions, ensuring load balance at the charging station and optimizing resource utilization.

[0189] The present invention also includes a fast charging demand guidance device for electric vehicle users' charging willingness, using the above method, such as Figure 6 As shown, including:

[0190] A dynamic traffic network establishment unit is used to obtain road, traffic information and charging station related information to establish a dynamic traffic network with real-time updates;

[0191] An impedance and power consumption model building unit is used to build a road traffic impedance model and an electric vehicle power consumption model based on a dynamic traffic network and taking into account the road travel time and road travel speed of electric vehicles under different traffic conditions;

[0192] The travel behavior model building unit is used to simulate the travel behavior of electric vehicles based on the road traffic impedance model and the electric vehicle power consumption model. According to the different travel patterns of electric vehicles, electric vehicles are divided into types, including private cars and online ride-hailing vehicles, and the private car travel behavior model and the online ride-hailing vehicle travel behavior model are built respectively.

[0193] The user satisfaction building unit is used to build a quantitative index of user charging satisfaction based on the private car travel behavior model and the online car-hailing travel behavior model, taking into account travel time, charging electricity price and electricity consumption;

[0194] The charging selection model building unit is used to propose charging selection models for different types of electric vehicles based on the quantitative indicators of user charging satisfaction and the queuing system of charging stations;

[0195] The dynamic pricing strategy unit is used to predict the charging load of charging stations in each time period based on the charging selection model and the queuing system of charging stations, and to formulate a dynamic pricing strategy for charging stations with the goal of minimizing the load fluctuation of each charging station.

[0196] See also Figure 7 A computer device 400 provided in an embodiment of the present application includes: a processor 410 and a memory 420, wherein the memory 420 stores a computer program executable by the processor 410, and when the computer program is executed by the processor 410, the above method is executed.

[0197] The embodiment of the present application further provides a storage medium 430 on which a computer program is stored. When the computer program is run by the processor 410, the above method is executed.

[0198] Among them, the storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0199] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0200] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0201] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0202] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0203] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0204] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0205] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0206] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for guiding the fast charging demand of electric vehicles, characterized in that: The steps include: S10: Obtain road, traffic information and charging station related information to establish a dynamic traffic network with real-time updates; S20: Based on the dynamic traffic network, considering the road travel time and road travel speed of electric vehicles under different traffic conditions, a road traffic impedance model and an electric vehicle power consumption model are constructed; S30: Based on the road traffic impedance model and the electric vehicle power consumption model, simulate the travel behavior of the electric vehicle, classify the electric vehicles into types according to different travel patterns of the electric vehicles, including private cars and online-hailing cars, and respectively construct a private car travel behavior model and an online-hailing car travel behavior model; S40: Based on the private car travel behavior model and the online car-hailing travel behavior model, a quantitative index of user charging satisfaction is constructed by comprehensively considering travel time, charging electricity price and electricity consumption; S50: Based on the quantitative index of user charging satisfaction and the queuing system of charging stations, a charging selection model for different types of electric vehicles is proposed; S60: Based on the charging selection model and the queuing system of the charging station, the charging load of the charging station in each time period is predicted, and a dynamic pricing strategy for the charging station is formulated with the goal of minimizing the load fluctuation of each charging station.

2. The electric vehicle fast charging demand guidance method according to claim 1, characterized in that: In step S20, the road traffic impedance model includes: In the formula, R l is the total travel time of section l; T 0,l is the travel time when the road section l is unobstructed; Q1 is the traffic path of the road section l; C l is the actual traffic capacity of section l; L l is the length of section l; N l is the number of roadside vehicles on section l; τ is the influence coefficient of roadside vehicles on the traffic of the section.

3. The method for guiding electric vehicle fast charging demand according to claim 1, characterized in that: In step S20, the electric vehicle power consumption model includes: In the formula, E lose,l is the power consumption of the electric vehicle on road section l; V l is the average speed of the electric vehicle on road section l. The speeds corresponding to different levels of roads and different road conditions are different; L l is the length of section l; a, b and c are power consumption coefficients.

4. The method for guiding electric vehicle fast charging demand according to claim 1, characterized in that: In step S30, electric vehicles are divided into types according to different travel patterns of electric vehicles, including private cars and online ride-hailing vehicles, and private car travel behavior models and online ride-hailing vehicle travel behavior models are constructed respectively, including: S31: Determine the travel behavior of private cars, including using trip chains with residential areas as the starting and ending points to describe the travel behavior of private cars. The first trip time and stay time of each type of trip chain follow a normal distribution; S32: Determine the travel behavior of the online-hailing vehicle, including describing the travel behavior of the online-hailing vehicle using a travel state transfer matrix, and the first travel time and the number of travels of the online-hailing vehicle follow a normal distribution; S33: Based on the travel behavior of the private car and the travel behavior of the online car-hailing vehicle, respectively planning the routes of the private car and the online car-hailing vehicle, including: When traveling, private cars plan their routes with the goal of searching for the shortest road travel time; When the online ride-hailing service is carrying passengers, the route is planned with the goal of minimizing the distance traveled by the electric vehicle. When the online ride-hailing service is empty, the route is planned with the goal of minimizing the energy consumption or road travel time of the electric vehicle. S34: Based on the planning of the routes of the private car and the online-hailing car, the arrival time and remaining power of the electric car are calculated.

5. The method for guiding electric vehicle fast charging demand according to claim 4, characterized in that: In step S34, the arrival time and remaining power of the electric vehicle are calculated, and the model includes: In the formula, is the time when the hth electric car arrives at the activity location p; is the time when the hth electric car leaves the activity location p-1; is the travel time from node i to node j; Ω p-1,p is the set of road segments set in the path from activity location p-1 to activity location p; is the remaining power of the hth electric vehicle when it arrives at the activity location p; is the remaining power of the hth electric vehicle when it leaves the activity location p-1; E lose，i,j is the power consumption of the road section from node i to node j.

6. The method for guiding electric vehicle fast charging demand according to claim 1, characterized in that: In step S40, the travel time, charging electricity price and power consumption are comprehensively considered to construct a quantitative index of user charging satisfaction, including: f CSR,k =αf 1,k +βf 2,k +γf 3,k ; In the formula, f CSR,k The comprehensive charging satisfaction of electric vehicles charging at charging station k; f 1,k is the charging travel time satisfaction of electric vehicles arriving at charging station k; f 2,k is the charging travel time satisfaction of electric vehicles arriving at charging station k; f 3,k is the satisfaction of the electric vehicle with the charging electricity price when it arrives at the charging station k; α, β and γ are the weight coefficients of the user's charging satisfaction index, and their sum is 1.

7. The electric vehicle fast charging demand guidance method according to claim 1, characterized in that: In step S50, based on the user charging satisfaction quantitative index and the queuing system of the charging station, a charging selection model for different types of electric vehicles is proposed, including: S51: Determine the charging demand of private cars, including before each trip, when the remaining power of the electric car is not enough to support the next destination, the electric car user will choose to detour to the charging station for charging; if fast charging is required during the trip between two stopovers, the private car will detour to the charging station for charging; S52: Determine the charging demand of online-hailing vehicles, including that when the remaining battery power of an online-hailing vehicle is lower than a certain indicator and the vehicle is in an unloaded state, it will go to a charging station to charge before carrying passengers; S53: According to the charging demand judgment results of the private car and the charging demand judgment results of the online car-hailing vehicle, the most suitable charging station is selected based on the user charging satisfaction quantitative index, and the charging time and load demand are calculated according to the initial power and charging station conditions; S54: According to the charging time and load demand, the queuing system of the charging station determines the queuing and charging order of the electric vehicles based on a first-come, first-served principle.

8. The method for guiding electric vehicle fast charging demand according to claim 1, characterized in that: In step S60, based on the charging selection model and the queuing system of the charging station, the charging load of the charging station in each time period is predicted, and the dynamic pricing strategy of the charging station with the goal of minimizing the load fluctuation of each charging station includes: S61: construct a charging station load prediction model to obtain a predicted charging load of the charging station; S62: According to the maximum charging load capacity that different charging stations can accommodate, the predicted load of each charging station is converted to the maximum capacity, and the average predicted load of the charging station is calculated; S63: When the difference between the predicted charging load of each charging station and the average predicted load is less than or greater than the adjustment threshold, adjusting the electricity price of the charging station; S64: According to the adjustment of the charging station electricity price, the charging electricity price adjustment step is optimized based on the particle swarm optimization algorithm with a compression factor.

9. The electric vehicle fast charging demand guidance method according to claim 8, characterized in that: In step S62, the average predicted load of the charging station is calculated, and the model includes: In the formula, is the average predicted load demand during period t; is the predicted load demand of charging station k in period t; k is the calculation factor of charging station k. When the charging load capacity of charging station k is equal to the maximum capacity of all charging stations, then σ k =1; K is the total number of charging stations; q k is the number of charging piles in charging station k.

10. The electric vehicle fast charging demand guidance method according to claim 8, characterized in that: In step S63, when the difference between the predicted charging load demand of each charging station and the average predicted load is less than or greater than the adjustment threshold, the electricity price of the charging station is adjusted. The model includes: In the formula, C k,t is the charging electricity price of charging station k in period t; Δc k is the charging price adjustment step; ε is the dead zone coefficient; is the average predicted load demand during period t; is the predicted load demand of charging station k in period t; k is the calculation factor of charging station k. When the charging load capacity of charging station k is equal to the maximum capacity of all charging stations, then σ k =1.

11. A device for guiding the fast charging demand of electric vehicles, characterized in that: Use of the method according to any one of claims 1 to 10, comprising: A dynamic traffic network establishment unit is used to obtain road, traffic information and charging station related information to establish a dynamic traffic network with real-time updates; An impedance and power consumption model building unit is used to build a road traffic impedance model and an electric vehicle power consumption model based on the dynamic traffic network and taking into account the road travel time and road travel speed of electric vehicles under different traffic conditions; A travel behavior model building unit is used to simulate the travel behavior of electric vehicles based on the road traffic impedance model and the electric vehicle power consumption model, and classify electric vehicles into types according to different travel patterns of electric vehicles, including private cars and online ride-hailing vehicles, and respectively build a private car travel behavior model and an online ride-hailing vehicle travel behavior model; A user satisfaction building unit, for building a quantitative index of user charging satisfaction based on the private car travel behavior model and the online car-hailing travel behavior model, taking into account travel time, charging electricity price and electricity consumption; A charging selection model building unit, used to propose charging selection models for different types of electric vehicles based on the user charging satisfaction quantitative index and the queuing system of the charging station; A dynamic pricing strategy unit is used to predict the charging load of the charging station in each time period based on the charging selection model and the queuing system of the charging station, and to formulate a dynamic pricing strategy for the charging station with the goal of minimizing the load fluctuation of each charging station.

12. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 10 is implemented.

13. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 10 is implemented.

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