Electric vehicle fast charging navigation method and device, electronic equipment and storage medium

Abstract

The application provides an electric vehicle fast charging navigation method and device, electronic equipment and storage medium, constructs a coupling network model of a traffic network-power grid in a region where a target electric vehicle user is located, a coupling edge is composed of a road node containing a charging station and a power grid node of the charging station, and the weight of the road node is defined as an average travel time, the weight of the power grid node is defined as a voltage variation, and both of them jointly determine a charging cost of the charging station; a charging navigation model is constructed with a target function of a comprehensive cost of the target electric vehicle user and constraint conditions of minimum residual allowable capacity of a battery and queue tolerance time; and through processing of the charging navigation model, a charging station with the lowest comprehensive cost is taken as a target charging station, and an optimal path to the target charging station is output. The application can realize best charging station recommendation and optimal path planning of the electric vehicle user by using multi-source information such as the traffic network-power grid, and promote benign collaborative operation of the traffic network-power grid coupling network.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle fast charging navigation technology, and more specifically, to an electric vehicle fast charging navigation method, device, electronic device, and storage medium. Background Technology

[0002] In recent years, electric vehicles have become a green mode of transportation to alleviate the energy crisis and environmental problems. Compared with traditional gasoline vehicles, electric vehicles have a shorter driving range due to battery capacity limitations, requiring users to charge their batteries during travel. Fast charging, due to its short charging time, is increasingly favored by users who need to quickly replenish energy during their journeys. However, influenced by factors such as road network structure, fast charging station layout, and user travel behavior, the fast charging demand of electric vehicle users exhibits significant randomness and spatiotemporal distribution characteristics.

[0003] Uncoordinated fast charging behavior, such as a large number of electric vehicles heading to the same fast charging station, can easily lead to severe queuing at the station, reducing user satisfaction, and causing the power grid to overload and jeopardize its safe and stable operation. Therefore, it is necessary to adopt appropriate charging navigation strategies to guide users to suitable charging stations based on their cost preferences, while balancing the utilization rate of each charging station, alleviating traffic congestion, and maintaining the safe and stable operation of the power grid. Summary of the Invention

[0004] In view of this, to solve the above problems, the present invention provides a method, device, electronic device, and storage medium for fast charging navigation of electric vehicles, the technical solution of which is as follows:

[0005] A fast-charging navigation method for electric vehicles, the method comprising:

[0006] Construct a coupled network model of the transportation network and the power grid in the area where the target electric vehicle user is located; wherein, in the coupled network model, the weight of the road node represents the average travel time, the weight of the power grid node represents the voltage change, and the coupling edge of the coupled network model is composed of the road node containing the charging station and the power grid node of the charging station;

[0007] Based on the weights of road nodes and power grid nodes in the coupled network model, the unit charging cost of the charging station in the coupled network model is determined.

[0008] Construct a charging navigation model for the target electric vehicle user; wherein the charging navigation model takes comprehensive cost as the objective function, minimum remaining battery capacity and queuing tolerance time as constraints, and the comprehensive cost includes at least the charging cost of the charging station in the coupled network model, and the charging cost is determined by its unit charging cost;

[0009] By processing the charging navigation model, the charging station with the lowest overall cost is determined as the target charging station, and the optimal path for the target electric vehicle user to reach the target charging station is output.

[0010] Preferably, the method for obtaining the weights of road nodes in the coupled network model includes:

[0011] For each road node in the coupled network model, identify the other road nodes connected to that road node;

[0012] Obtain the zero-flow passage time and congestion level of the road connecting this road node to other road nodes at a first specified time.

[0013] Based on the obtained zero-flow passage time and congestion level, calculate the average passage time of the road connecting the road node to other road nodes at the first specified time.

[0014] The weight of the road node at the first specified time is calculated based on the number of other road nodes and the average travel time of the road connecting the road node to other road nodes at the first specified time.

[0015] Preferably, the method for obtaining the weights of the power grid nodes in the coupled network model includes:

[0016] For each grid node in the coupled network model, the voltage change caused by an electric vehicle user connecting to the grid node at a second specified time is calculated using the power flow balance formula.

[0017] Calculate the weight of the grid node at a second specified time based on the voltage change.

[0018] Preferably, determining the unit charging cost of a charging station in the coupled network model based on the weights of road nodes and power grid nodes in the coupled network model includes:

[0019] For each charging station in the coupled network model, obtain the weight of the road node where the charging station is located and the weight of the power grid node it is connected to;

[0020] The real-time service fee for the charging station is calculated based on the weights of the road nodes and the power grid nodes.

[0021] Obtain the real-time grid electricity price, and calculate the unit charging cost of the charging station based on the real-time grid electricity price and the real-time service fee of the charging station.

[0022] Preferably, the method further includes:

[0023] Get navigation update instructions;

[0024] In response to the navigation update command, the step of constructing a coupled network model of the transportation network and power grid in the area where the target electric vehicle user is located is executed to output the latest target charging station and optimal route.

[0025] Preferably, the overall cost also includes: the arrival time cost, queuing time cost, and charging time cost of the charging station in the coupled network model.

[0026] A fast-charging navigation device for electric vehicles, the device comprising:

[0027] The coupled network model construction module is used to construct a coupled network model of the traffic network and the power grid in the area where the target electric vehicle user is located; wherein, in the coupled network model, the weight of the road node represents the average travel time, the weight of the power grid node represents the voltage change, and the coupling edge of the coupled network model is composed of the road node containing the charging station and the power grid node of the charging station;

[0028] The unit charging cost determination module is used to determine the unit charging cost of the charging station in the coupled network model based on the weights of the road nodes and the weights of the power grid nodes in the coupled network model.

[0029] A charging navigation model construction module is used to construct a charging navigation model for the target electric vehicle user; wherein, the charging navigation model takes comprehensive cost as the objective function and minimum remaining allowable battery capacity and queuing tolerance time as constraints, and the comprehensive cost includes at least the charging cost of the charging station in the coupled network model, and the charging cost is determined by its unit charging cost;

[0030] The navigation module is used to process the charging navigation model, determine the charging station with the lowest overall cost as the target charging station, and output the optimal path for the target electric vehicle user to reach the target charging station.

[0031] Preferably, the navigation module is further configured to:

[0032] Get navigation update instructions;

[0033] In response to the navigation update command, the coupled network model construction module is triggered to output the latest target charging station and optimal path.

[0034] An electronic device includes: at least one memory and at least one processor; the memory stores an application program, and the processor calls the application program stored in the memory, the application program being used to implement the electric vehicle fast charging navigation method.

[0035] A storage medium storing computer program code, which, when executed, implements the electric vehicle fast charging navigation method.

[0036] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0037] This invention provides a fast-charging navigation method, device, electronic device, and storage medium for electric vehicles. First, a coupled network model of the transportation network and power grid within the area where the target electric vehicle user is located is constructed. The coupling edges consist of road nodes containing charging stations and power grid nodes of those charging stations. The weight of the road nodes is defined as the average travel time, and the weight of the power grid nodes is defined as the voltage change; both jointly determine the charging cost of the charging station. Next, a charging navigation model is constructed with the overall cost of the target electric vehicle user as the objective function, constrained by the minimum remaining battery capacity and queuing tolerance time. Finally, by processing the charging navigation model, the charging station with the lowest overall cost is selected as the target charging station, and the optimal path to the target charging station is output. This invention utilizes multi-source information such as transportation network and power grid to achieve optimal charging station recommendation and optimal path planning for electric vehicle users, and promotes the healthy coordinated operation of the transportation network and power grid coupled network. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0039] Figure 1 This is a flowchart of the electric vehicle fast charging navigation method provided in an embodiment of the present invention;

[0040] Figure 2 This is a structural block diagram of an electric vehicle fast charging navigation system provided in an embodiment of the present invention;

[0041] Figure 3 This is a schematic diagram of the structure of the electric vehicle fast charging navigation device provided in an embodiment of the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0044] Electric vehicle navigation technology refers to the use of appropriate technologies to guide electric vehicles to suitable charging stations for charging. Its core idea lies in utilizing multi-source information such as transportation network information, power grid information, or a combination of both to recommend charging stations and plan the optimal route to them. Specifically, using single-source transportation network information, optimal route planning (e.g., shortest time, shortest distance) can be achieved for electric vehicle users to reach fast-charging stations; using single-source power grid information, the most suitable charging station can be recommended from the perspective of optimizing power grid operation; and by using multi-source information such as transportation network and power grid information and providing reasonable guidance, not only can the best charging station recommendation and optimal route planning be achieved, but the healthy and coordinated operation of the transportation network and power grid coupled network can also be promoted.

[0045] Furthermore, given the inherently selfish nature of electric vehicle users, effective incentive mechanisms are needed to encourage their participation in orderly navigation. One widely used incentive mechanism is the pricing mechanism, where charging stations adjust charging prices to benefit users, thereby altering their charging behavior.

[0046] Existing navigation methods largely consider a linear relationship between the unit charging price and the total charging volume of electric vehicles at a charging station at a given time, or use the Locational Marginal Price (LMP) as the unit charging price. These pricing methods, considering the singular interaction between electric vehicles and the power grid, fail to reflect the coupling between the transportation network and the power grid, and are detrimental to the coordinated operation of the coupled network. In other words, using linear charging volume or LMP to set the unit price for charging stations fails to reflect the coupling between the power grid and the transportation network in scenarios of large-scale electric vehicle penetration, and is also detrimental to the optimized operation of the coupled network. The main reason is that these pricing mechanisms only consider the interaction between electric vehicles and the power grid or charging stations, neglecting the interaction between electric vehicles and the transportation network, or focusing solely on the safety and stability of the power grid while ignoring the fact that electric vehicles are vehicles. Navigation strategies based on such pricing mechanisms are prone to congestion at road nodes where fast-charging stations are located, thus affecting the normal operation of nearby traffic.

[0047] Existing navigation methods largely rely on initial information to form fixed navigation strategies, neglecting the dynamic nature of traffic and power grid information. Dynamic information directly impacts the optimal strategy, therefore, a navigation strategy based on initial information may not be optimal. In other words, a navigation strategy based on initial information as the final strategy may not be optimal. The main reason is that information within the coupled network exhibits complex and dynamic changes, such as traffic conditions (temporary traffic control, road accidents, etc.). The optimal navigation strategy needs to be adjusted and updated promptly based on real-time information, but the navigation strategy based on initial information fails to consider this complex dynamic change and treats it as static.

[0048] To address the aforementioned issues, this invention proposes a navigation strategy for electric vehicles (EVs) suitable for fast charging stations (FCS), taking into account the coupling state of the transportation network and the power grid. Furthermore, it designs the charging station service fee from the perspective of reflecting the operation of the coupled network, and dynamically adjusts and updates the navigation strategy based on the coupled network information.

[0049] Specifically:

[0050] This invention first uses graph theory to represent the transportation network and power grid of the study area, and uses an adjacency matrix to reflect the coupling relationship between the power grid and the transportation network. Second, the weight of a road node (also called a transportation network node or road network node) is defined as the average travel time of that node at a certain moment, and the weight of a power grid node is defined as the voltage change caused by the power grid node where the electric vehicle user connects to the charging station. These two weights together determine the charging cost (also called the charging station service fee). Next, a charging navigation model is established with the minimum comprehensive cost for a single user (charging cost, arrival time cost, queuing time cost, and charging time cost), and constraints of the minimum remaining battery capacity and queuing tolerance time. Finally, the shortest path algorithm is used to select the charging station with the lowest cost as the target charging station, and the optimal path to the target charging station is obtained. Furthermore, it should be noted that the navigation strategy is continuously updated based on information until the user reaches the target charging station before reaching the next road node.

[0051] See Figure 1 , Figure 1 This is a flowchart illustrating the fast-charging navigation method for electric vehicles provided in an embodiment of the present invention. Figure 1 As shown, the electric vehicle fast charging navigation method provided in this embodiment of the invention includes the following steps:

[0052] S10, construct a coupled network model of the transportation network and the power grid in the area where the target electric vehicle user is located; wherein, the weight of the road node in the coupled network model represents the average travel time, the weight of the power grid node represents the voltage change, and the coupled edge of the coupled network model is composed of the road node containing the charging station and the power grid node of the charging station.

[0053] The electric vehicle fast charging navigation method provided in this invention can be applied to an information processing center, where the target electric vehicle user is an electric vehicle user within the coverage area of ​​the information processing center. Specifically, graph theory can be used to represent the transportation network and power grid of the research area to obtain dynamic transportation network and power grid models; then, an adjacency matrix is ​​used to reflect the coupling relationship between the power grid and the transportation network to construct a coupled network model of the transportation network and the power grid.

[0054] The transportation network model can be expressed as the following formula (1):

[0055]

[0056] In the above formula, G R V represents the transportation network (i.e., the transportation network) within the coverage area of ​​the information processing center; R u in the transportation network R A finite set of road nodes; E R For all road segments; W R This is the set of road node weights, defined here as the average travel time through the road nodes; The node at time t The weight.

[0057] The power grid model can be expressed as the following formula (2):

[0058]

[0059] In the above formula, G G V represents the power distribution network (i.e., the power grid) within the coverage area of ​​the information processing center; G For the distribution network u G A finite set of power grid nodes; E G W is the set of all edges of transmission lines in the power grid. G This is the set of weights for power grid nodes, defined here as the voltage change of the nodes; The node at time t The weight.

[0060] The coupled network model of the transportation network and the power grid can be expressed as the following formula (3):

[0061]

[0062] In the above formula, Υ represents a coupled network consisting of a set of network layers Γ and a set of edges Z; δ is the network layer number, and G is defined as... δ =(V δ E δ ) represents the network layer numbered δ (for example, a value of 1 represents a transportation network, while a value of 2 represents a power grid), V δ E δ These represent the set of nodes and the set of edges in the delta network layer, respectively; E δγ For the coupling edge between the transportation network and the power grid (i.e., the edge formed by the coupling of nodes in the two networks), the coupling method is defined as the road node containing a charging station forming a coupling edge with the power grid node to which the charging station is connected. Therefore, with E... δγ The corresponding interlayer coupling edge matrix M<δ,γ>={m ij <δ,γ>},m ij <δ,γ> represents the coupling relationship between the two network nodes, and its expression is as follows: (4)

[0063]

[0064] That is, if road node i is coupled to power grid node j, then m ij <δ,γ>=1, otherwise m ij <δ,γ>=0.

[0065] The adjacency matrix M of the coupled network is defined as follows (5):

[0066]

[0067] In the above formula, M(δ) and M(γ) represent the adjacency matrices of the two networks that constitute the coupled network.

[0068] See Figure 2 , Figure 2 This is a structural block diagram of an electric vehicle fast-charging navigation system provided in an embodiment of the present invention. Figure 2 As shown, the intelligent transportation system dynamically updates the traffic network topology and real-time traffic information. The information processing center can obtain the weights of road nodes (i.e., ...) from the intelligent transportation system. Figure 2 The traffic network model is constructed using road network node weights and real-time traffic information. Electric vehicles can use charging stations (i.e., charging stations) Figure 2 Charging occurs at fast-charging stations (in the network), allowing the power grid operation center to obtain real-time charging load and dynamically update the distribution network topology, base load, and node voltage. The information processing center can obtain the weights of the power grid nodes (i.e., ...) from the power grid operation center. Figure 2 The power grid model is constructed by using the weights of power grid nodes and real-time power grid prices.

[0069] Furthermore, the weights of road nodes in the coupled network model can be obtained in the following manner in this embodiment of the invention:

[0070] For each road node in the coupled network model, identify the other road nodes connected to that road node; obtain the zero-flow passage time and congestion level of the roads connecting that road node to other road nodes at a first specified time; calculate the average passage time of the roads connecting that road node to other road nodes at the first specified time based on the obtained zero-flow passage time and congestion level; calculate the weight of that road node at the first specified time based on the number of other road nodes and the average passage time of the roads connecting that road node to other road nodes at the first specified time.

[0071] Specifically, the following formula (6) can be used to calculate the road nodes. Weight at time t

[0072]

[0073] in,

[0074] In the above formula, J represents the road node. The number of directly connected road nodes; Indicates the connection of road nodes The average travel time of road ij at time t; The zero-flow passage time for road ij; ξ ij (t) represents the congestion level of road ij at time t.

[0075] In other words, for any road node In other words, in determining other road nodes directly connected to it Then, obtain the zero-flow passage time for both at time t. and congestion ξ ij (t), calculate the road node according to the above formula (6). The weight at time t.

[0076] Furthermore, the weights of the power grid nodes in the coupled network model can be obtained in the following manner in this embodiment of the invention:

[0077] For each grid node in the coupled network model, the voltage change caused by an electric vehicle user connecting to the grid node at a second specified time is calculated using the power flow balance formula; based on the voltage change, the weight of the grid node at the second specified time is calculated.

[0078] Specifically, the following formula (7) can be used to calculate the power grid nodes. Weight at time t

[0079]

[0080] In the above formula, This indicates that electric vehicle user n connects to the grid node at time t. The amount of voltage change that causes this node; χ g This is a weighting coefficient that can be set according to the requirements of the impact of node voltage changes on the security and stability of the power grid.

[0081] It should be noted that the node voltage in the power grid can be calculated using the power flow balance formula, as shown in the following formula (8):

[0082]

[0083] In the above formula, Respectively, power grid nodes Injected active power and reactive power at time t; Respectively, power grid nodes The active and reactive power of the base load at time t; At time t, at the power grid node The charging power of electric vehicles connected at the point; The power grid nodes at time t are respectively With grid nodes Connected power grid nodes The voltage; Representing grid nodes With grid nodes The electrical conductance and susceptance between them; Represents grid node v i G and power grid nodes The phase angle difference between them.

[0084] If the charging power of electric vehicle user n is known, substituting it into equation (8) above, the impact on the grid nodes before and after user n's connection can be calculated. Magnitude of voltage change

[0085] It should be noted that in this embodiment of the invention, the second specified time and the first specified time can be the same time, so as to ensure the real-time performance of the coupled network model.

[0086] S20. Based on the weights of road nodes and power grid nodes in the coupled network model, determine the unit charging cost of the charging station in the coupled network model.

[0087] In this embodiment of the invention, the information processing center determines the real-time service fee for charging stations by utilizing the weights of road nodes and power grid nodes. It should be noted that the weight of the road node refers to the weight of the road node where the charging station is located, and the weight of the power grid node refers to the weight of the power grid node to which the charging station is connected.

[0088] In the specific implementation process, step S20, "determining the unit charging cost of the charging station in the coupled network model based on the weights of the road nodes and the power grid nodes in the coupled network model," can be achieved through the following steps:

[0089] For each charging station in the coupled network model, obtain the weight of the road node where the charging station is located and the weight of the power grid node it is connected to; calculate the real-time service fee of the charging station based on the obtained weights of the road node and the power grid node; obtain the real-time power grid price, and calculate the unit charging cost of the charging station based on the real-time power grid price and the real-time service fee of the charging station.

[0090] In this embodiment of the invention, the real-time service fee of the charging station can be calculated using the following formula (9):

[0091]

[0092] In the above formula, χ is the weight coefficient of the coupled network, which can be adjusted according to the degree of coupling between the transportation network and the power grid. Generally, it can be set to 0.5. Let ρ be the basic service fee for charging station k at time t. The unit charging cost ρ for charging station k at time t is... k (t) includes real-time grid electricity price and real-time service fees The two parts can be calculated using the following formula (10):

[0093]

[0094] In other words, for any charging station k, the weight of its road node is determined. and the weight of the connected power grid nodes. Then, the real-time service fee for charging station k is calculated according to the above formula (9). And obtain real-time grid electricity price Then, the unit charging cost ρ of charging station k is calculated according to the above formula (10). k (t).

[0095] S30, construct a charging navigation model for the target electric vehicle users; wherein, the charging navigation model takes the comprehensive cost as the objective function and the minimum remaining allowable battery capacity and queuing tolerance time as constraints, and the comprehensive cost includes at least the charging cost of the charging station in the coupled network model, and the charging cost is determined by its unit charging cost.

[0096] In this embodiment of the invention, the overall cost for an electric vehicle user includes at least the charging fee at the charging station, which can be determined by its unit charging fee. Specifically, the target electric vehicle user needs to pay the charging station k a charging fee to complete charging at charging station k. It can be calculated using the following formula (11):

[0097]

[0098] In the above formula, The target charging capacity for target electric vehicle users; E represents the remaining battery power of the target electric vehicle user after arriving at charging station k; n The rated battery capacity for the target electric vehicle user.

[0099] In some scenarios, the overall cost can also include the arrival time cost, queuing time cost, and charging time cost of the charging station in the coupled network model. In other words, the overall cost for electric vehicle users can include the time cost required for the electric vehicle to reach the charging station (i.e., arrival time cost), the queuing time cost (i.e., queuing time cost), the charging time cost, and the charging fee.

[0100] In this embodiment of the invention, a charging navigation model is established with the overall cost as the objective function and the minimum remaining allowable battery capacity and queuing tolerance time as constraints. The objective function expression of the charging navigation model for a single electric vehicle user is shown in the following formula (12):

[0101]

[0102] In the above formula, This represents the charging fee paid by the target electric vehicle user to charging station k; Let π represent the time required for the target electric vehicle user to reach charging station k (i.e., arrival time cost), the queuing time (i.e., queuing time cost), and the time required to charge to the target level (i.e., charging time cost), respectively; π is the coefficient for converting time into money, which can be set according to the average annual hourly wage of Chinese residents; α and β are the preference coefficients of the target electric vehicle user for charging fees and time costs, respectively. α and β can be set according to the electric vehicle user's preferences, and their sum should be equal to 1; K is the set of all charging stations.

[0103] The constraint expression for the charging navigation model for a single electric vehicle user is shown in the following formula (13):

[0104]

[0105] In the above formula, x ij For path decision variables (a value of 1 means that the target electric vehicle user will travel from road node i to road node j; a value of 0 means that the road segment between road node i and road node j is not selected); ΔE represents the current remaining battery power for the target electric vehicle user. n Energy consumption per unit; It is the minimum allowable charge level set for target electric vehicle users to avoid range anxiety and protect battery life; The allowable waiting time for target electric vehicle users at charging stations.

[0106] In addition, the time required for the target electric vehicle user to travel from the current road node i to the charging station k. The arrival time of the charging station can be calculated using the following formula (14):

[0107]

[0108] Based on the charging time initiated by the target electric vehicle user The time when the target electric vehicle user arrives at charging station k can be determined. The following formula (15) is used for calculation:

[0109]

[0110] The time required for the target electric vehicle user to charge to the target battery level after arriving at charging station k. (i.e., charging time cost) can be calculated using the following formula (16):

[0111]

[0112] In the above formula, p k Let be the charging power of the charging piles in charging station k. Here, it is assumed that all charging piles in the same charging station have the same power.

[0113] The waiting time for the target electric vehicle user to charge after arriving at charging station k (i.e., queuing time cost) can be calculated using the following formula (17):

[0114]

[0115] In the above formula, The total time required for the target electric vehicle user to complete charging at charging station k is determined according to the following formulas (18), (19), and (20):

[0116] like

[0117]

[0118] like

[0119]

[0120] like

[0121]

[0122] In the above formula, For target electric vehicle users The number of electric vehicles that are charging at charging station k when the time arrives; The queue number of the target electric vehicle user when arriving at charging station k; for The set of remaining charging times for all electric vehicles; function For from set Selected from Small values; For charging stations, the first one in the k-line is listed. The total time required for each user to complete charging.

[0123] S40, by processing the charging navigation model, determines the charging station with the lowest overall cost as the target charging station and outputs the optimal path for the target electric vehicle user to reach the target charging station.

[0124] In this embodiment of the invention, after obtaining all the components of the overall cost, the shortest path algorithm can be used to solve the charging navigation model to obtain the charging station with the minimum overall cost as the initial target charging station for navigation. Then, the information processing center sends the target charging station and its corresponding optimal path to the target electric vehicle user, who can then proceed to the next road node according to the navigation route.

[0125] It should be noted that, in solving the charging navigation model, other optimization algorithms suitable for this scheme besides the shortest path algorithm can also be used in this embodiment of the invention, and this embodiment of the invention does not limit this.

[0126] Based on this, embodiments of the present invention may further include the following steps to achieve the purpose of updating the charging navigation strategy:

[0127] Obtain the navigation update command; in response to the navigation update command, execute the above steps S10 to S40 to output the latest target charging station and optimal route.

[0128] In this embodiment of the invention, the power grid, transportation network, and charging station information are updated. Before the target electric vehicle user reaches the intersection of the next road node, the information processing center can recalculate the navigation strategy (including charging station selection and route planning) using the updated information. This involves a rolling update of the navigation strategy. If the calculated navigation strategy remains consistent with the strategy from the previous period, no further strategy information is pushed to the user; if the navigation strategy changes, an updated strategy is sent. This step is repeated until the next road node is a road node containing a charging station.

[0129] See also Figure 2 Target electric vehicle users (i.e. Figure 2 The electric vehicle terminal (EV terminal) can provide the information processing center with EV information such as location, current battery level, and battery capacity, as well as charging decisions such as preference coefficient, minimum allowable battery level, and waiting time. The charging station provides the information processing center with information such as queuing time and location. The information processing center can then return navigation information to the EV terminal, navigation information containing the target charging station and optimal route to the target EV user, and the charging decision (i.e., charging time) to the charging station. Figure 2 (China's electric vehicle charging policy).

[0130] The electric vehicle fast charging navigation method provided in this invention can utilize multi-source information such as transportation network and power grid to recommend the best charging station and plan the optimal route for electric vehicle users, and promote the benign coordinated operation of the transportation network and power grid coupled network.

[0131] Based on the electric vehicle fast charging navigation method provided in the above embodiments, this invention also provides an apparatus for executing the electric vehicle fast charging navigation method, the structural schematic diagram of which is shown below. Figure 3 As shown, it includes:

[0132] The coupled network model construction module 10 is used to construct a coupled network model of the traffic network and the power grid in the area where the target electric vehicle user is located. In the coupled network model, the weight of the road node represents the average travel time, the weight of the power grid node represents the voltage change, and the coupled edge of the coupled network model is composed of the road node containing the charging station and the power grid node of the charging station.

[0133] The unit charging cost determination module 20 is used to determine the unit charging cost of a charging station in the coupled network model based on the weights of road nodes and power grid nodes in the coupled network model.

[0134] The charging navigation model construction module 30 is used to construct a charging navigation model for the target electric vehicle user. The charging navigation model takes the comprehensive cost as the objective function and the minimum remaining allowable battery capacity and queuing tolerance time as constraints. The comprehensive cost includes at least the charging cost of the charging station in the coupled network model. The charging cost is determined by its unit charging cost.

[0135] The navigation module 40 is used to determine the charging station with the lowest overall cost as the target charging station by processing the charging navigation model, and to output the optimal path for the target electric vehicle user to reach the target charging station.

[0136] Optionally, the coupled network model building module 10 obtains the weights of road nodes in the coupled network model in the following ways:

[0137] For each road node in the coupled network model, identify the other road nodes connected to that road node; obtain the zero-flow passage time and congestion level of the roads connecting that road node to other road nodes at a first specified time; calculate the average passage time of the roads connecting that road node to other road nodes at the first specified time based on the obtained zero-flow passage time and congestion level; calculate the weight of that road node at the first specified time based on the number of other road nodes and the average passage time of the roads connecting that road node to other road nodes at the first specified time.

[0138] Optionally, the coupled network model construction module 10 obtains the weights of the power grid nodes in the coupled network model in the following ways:

[0139] For each grid node in the coupled network model, the voltage change caused by an electric vehicle user connecting to the grid node at a second specified time is calculated using the power flow balance formula; based on the voltage change, the weight of the grid node at the second specified time is calculated.

[0140] Optional, the unit charging cost determination module 20 is specifically used for:

[0141] For each charging station in the coupled network model, obtain the weight of the road node where the charging station is located and the weight of the power grid node it is connected to; calculate the real-time service fee of the charging station based on the obtained weights of the road node and the power grid node; obtain the real-time power grid price, and calculate the unit charging cost of the charging station based on the real-time power grid price and the real-time service fee of the charging station.

[0142] Optionally, the navigation module 40 is also used for:

[0143] Obtain navigation update instructions; respond to navigation update instructions to trigger the coupled network model construction module 10 to output the latest target charging station and optimal path.

[0144] It should be noted that the detailed functions of each module in the embodiments of the present invention can be found in the corresponding disclosures of the above embodiments of the electric vehicle fast charging navigation method, and will not be repeated here.

[0145] Based on the electric vehicle fast charging navigation method provided in the above embodiments, this invention also provides an electronic device, which includes: at least one memory and at least one processor; the memory stores an application program, and the processor calls the application program stored in the memory, the application program being used to implement the electric vehicle fast charging navigation method.

[0146] Based on the electric vehicle fast charging navigation method provided in the above embodiments, this invention also provides a storage medium storing computer program code, which implements the electric vehicle fast charging navigation method when executed.

[0147] The present invention provides a detailed description of a fast-charging navigation method, device, electronic device, and storage medium for electric vehicles. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, those skilled in the art will recognize that there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

[0148] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0149] It should also be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that elements inherent to a process, method, article, or apparatus that comprises a list of elements, or elements inherent to such processes, methods, articles, or apparatus, are also included. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0150] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those 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 invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A fast-charging navigation method for electric vehicles, characterized in that, The method includes: Construct a coupled network model of the transportation network and the power grid in the area where the target electric vehicle user is located; wherein, in the coupled network model, the weight of the road node represents the average travel time, the weight of the power grid node represents the voltage change, and the coupling edge of the coupled network model is composed of the road node containing the charging station and the power grid node of the charging station; For each charging station in the coupled network model, obtain the weight of the road node where the charging station is located and the weight of the power grid node it is connected to; The real-time service fee for the charging station is calculated based on the weights of the road nodes and the power grid nodes. Obtain the real-time grid electricity price, and calculate the unit charging cost of the charging station based on the real-time grid electricity price and the real-time service fee of the charging station; Construct a charging navigation model for the target electric vehicle user; wherein the charging navigation model takes comprehensive cost as the objective function, minimum remaining battery capacity and queuing tolerance time as constraints, and the comprehensive cost includes at least the charging cost of the charging station in the coupled network model, and the charging cost is determined by its unit charging cost; By processing the charging navigation model, the charging station with the lowest overall cost is determined as the target charging station, and the optimal path for the target electric vehicle user to reach the target charging station is output.

2. The method according to claim 1, characterized in that, The methods for obtaining the weights of road nodes in the coupled network model include: For each road node in the coupled network model, identify the other road nodes connected to that road node; Obtain the zero-flow passage time and congestion level of the road connecting this road node to other road nodes at a first specified time. Based on the obtained zero-flow passage time and congestion level, calculate the average passage time of the road connecting the road node to other road nodes at the first specified time. The weight of the road node at the first specified time is calculated based on the number of other road nodes and the average travel time of the road connecting the road node to other road nodes at the first specified time.

3. The method according to claim 1, characterized in that, The methods for obtaining the weights of the power grid nodes in the coupled network model include: For each grid node in the coupled network model, the voltage change caused by an electric vehicle user connecting to the grid node at a second specified time is calculated using the power flow balance formula. Calculate the weight of the grid node at a second specified time based on the voltage change.

4. The method according to claim 1, characterized in that, The method further includes: Get navigation update instructions; In response to the navigation update command, the step of constructing a coupled network model of the transportation network and power grid in the area where the target electric vehicle user is located is executed to output the latest target charging station and optimal route.

5. The method according to claim 1, characterized in that, The overall cost also includes the arrival time cost, queuing time cost, and charging time cost of the charging station in the coupled network model.

6. A fast-charging navigation device for electric vehicles, characterized in that, The device includes: The coupled network model construction module is used to construct a coupled network model of the traffic network and the power grid in the area where the target electric vehicle user is located; wherein, in the coupled network model, the weight of the road node represents the average travel time, the weight of the power grid node represents the voltage change, and the coupling edge of the coupled network model is composed of the road node containing the charging station and the power grid node of the charging station; The unit charging cost determination module is used to obtain the weight of the road node where the charging station is located and the weight of the power grid node it is connected to for each charging station in the coupled network model; calculate the real-time service fee of the charging station based on the obtained weights of the road node and the power grid node; obtain the real-time power grid price and calculate the unit charging cost of the charging station based on the real-time power grid price and the real-time service fee of the charging station. A charging navigation model construction module is used to construct a charging navigation model for the target electric vehicle user; wherein, the charging navigation model takes comprehensive cost as the objective function and minimum remaining allowable battery capacity and queuing tolerance time as constraints, and the comprehensive cost includes at least the charging cost of the charging station in the coupled network model, and the charging cost is determined by its unit charging cost; The navigation module is used to process the charging navigation model, determine the charging station with the lowest overall cost as the target charging station, and output the optimal path for the target electric vehicle user to reach the target charging station.

7. The apparatus according to claim 6, characterized in that, The navigation module is also used for: Get navigation update instructions; In response to the navigation update command, the coupled network model construction module is triggered to output the latest target charging station and optimal path.

8. An electronic device, characterized in that, The electronic device includes: at least one memory and at least one processor; the memory stores an application program, and the processor calls the application program stored in the memory, the application program being used to implement the electric vehicle fast charging navigation method according to any one of claims 1-5.

9. A storage medium, characterized in that, The storage medium stores computer program code, which, when executed, implements the electric vehicle fast charging navigation method according to any one of claims 1-5.

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