A New Energy Vehicle Charging Guidance Method and System Based on Controlled Petri Nets

Abstract

The present invention discloses a new energy vehicle charging guidance method and system based on a controlled Petri net, belonging to the technical field of new energy vehicle charging guidance, and comprising the following steps: constructing an extended road network topology map according to the surrounding map information of the current position of the new energy vehicle and the user side and grid side information; constructing a Petri net model containing charging station information by adding electrical attributes to the charging station nodes according to the extended road network topology map; adding a Petri net structure controller to the Petri net model containing charging station information according to the charging station capacity information and traffic condition information; using the depth-first search method and the ILP method to perform an optimal search of distance, road condition and cost on the reachable space of the controlled Petri net model to obtain an optimal transition sequence; screening an optimal transition sequence by using an evaluation function in the optimal transition sequence, and the optimal transition sequence is the optimal path of the new energy vehicle from the starting mark through the optimal charging station mark set to the ending mark.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy vehicle charging guidance, and particularly relates to a new energy vehicle charging guidance method and system based on a controlled Petri net. Background Art

[0002] With the continuous development and progress of social technology, new energy vehicles with both electrical and transportation attributes are accelerating in popularity, and their charging will generate a large load on the operation of the power grid.

[0003] The problem of battery life during the journey of new energy vehicles is also one of the disadvantages compared to traditional gasoline vehicles. Limited by the existing battery technology, new energy vehicles often need to be charged frequently during a long-distance journey. In such a charging guidance problem, not only the charging pile guidance on the user side needs to be considered, but also the load balancing problem on the grid side needs to be considered. Therefore, the new energy vehicle charging guidance technology is becoming one of the key technologies in the development of the new energy vehicle industry.

[0004] In the existing charging guidance methods, there are those based on intelligent algorithms, analytical algorithms, and deep learning methods, etc. Among them, the solution quality and speed of intelligent algorithms depend on the design of relevant parameters and are difficult to be universal; after changing the actual environment such as vehicle models and road models, analytical algorithms often need to reconstruct their mathematical models and do not have universality; deep learning methods rely too much on dataset training and require a certain investment in time cost. These methods all have relatively large defects of their own, so there are still many problems in practical applications. Summary of the Invention

[0005] The purpose of the present invention is to overcome the problems of poor universality and long solution time of the existing new energy vehicle charging guidance methods, and propose a new energy vehicle charging guidance method and system based on a controlled Petri net.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] In the first aspect, the present invention provides a new energy vehicle charging guidance method based on a controlled Petri net, including the following steps: constructing an extended road network topology map according to the surrounding map information of the current position of the new energy vehicle and the user-side and grid-side information; the user-side and grid-side information includes charging station capacity information and traffic condition information;

[0008] According to the extended road network topology map, constructing a Petri net model containing charging station information by adding electrical attributes to the charging station nodes;

[0009] Adding a Petri net structure controller to the Petri net model containing charging station information according to the charging station capacity information and traffic condition information to obtain a controlled Petri net model;

[0010] Using the depth - first search method and the ILP method, perform an optimal search for distance, road conditions, and cost on the reachable space of the controlled Petri net model to obtain an optimal transition sequence;

[0011] In the optimal transition sequence, use an evaluation function to screen the optimal transition sequence. The optimal transition sequence is the optimal path of the new - energy vehicle from the starting marking through the optimal charging - station marking set to the ending marking. Output the path of the optimal transition sequence in the road network nodes of the extended road - network topology map to guide the new - energy vehicle to the charging station.

[0012] Further, constructing the extended road - network topology map according to the surrounding map information of the current position of the new - energy vehicle and the user - side and grid - side information is specifically as follows:

[0013] Obtain the surrounding map information according to the current position of the new - energy vehicle, use the surrounding map information to construct a road - network topology map, obtain the user - side and grid - side information, and add the user - side and grid - side information to the road - network topology map to construct the extended road - network topology map;

[0014] When using the surrounding map information to construct the road - network topology map, abstract the intersections in the surrounding map information as road - network nodes, and abstract the roads between two intersections as the edges between two nodes to obtain the road - network topology map.

[0015] Further, the user - side and grid - side information also includes trip information and charging - station location information;

[0016] The trip information includes the current - position information of the new - energy vehicle and the target - location information;

[0017] The charging - station location information and charging - station capacity information include the charging - station location information matching the charging type of the new - energy vehicle. The charging - station capacity information includes the remaining charging - pile information of the charging station. Each charging - station capacity information is represented by a binary tuple, and the binary tuple includes the actual number of used charging piles and the maximum number of charging piles that the charging station can use;

[0018] The traffic - road - condition information includes not - allowed - to - pass and allowed - to - pass. Allowed - to - pass includes three traffic - condition levels: unobstructed, relatively congested, and congested;

[0019] When adding the user - side and grid - side information to the road - network topology map to construct the extended road - network topology map, abstract the current - position information of the new - energy vehicle as the starting node, abstract the target - location information as the ending node, abstract the charging - station location information and charging - station capacity information as the charging - station node set, and assign congestion coefficients to the road - passing conditions between current nodes according to the traffic - road - condition information to obtain the extended road - network topology map.

[0020] Furthermore, the depth-first search and ILP method are used to perform an optimal search for distance, road conditions, and cost in the reachable space of the controlled Petri net model to obtain an optimal transition sequence, specifically as follows:

[0021] Use the depth-first search algorithm to search for the charging station identifiers reachable by the remaining mileage of the new energy vehicle to obtain a set of charging station identifiers, and evaluate the cost of the set of charging station identifiers to obtain the optimal transition sequence of the set of charging station identifiers;

[0022] Use the termination identifier to represent the final destination where the new energy vehicle stays. Calculate the minimum cost sequence between the set of charging station identifiers and the termination identifier through the ILP method, and concatenate the optimal transition sequence of the set of charging station identifiers and the minimum cost sequence between the set of charging station identifiers and the termination identifier to obtain the optimal transition sequence.

[0023] Furthermore, the Petri net model containing charging station information is as follows:

[0024] (N, M 0 )

[0025] N = (P′, T′, Pre′, Post', S, W)

[0026] P′ = P ∪ P e

[0027] T′ = T ∪ T e

[0028] Pre′: P′ × T′ → N

[0029] Post': P′ × T′ → N

[0030] S = [s 1 , s 2 , …, s |T|

[0031] W = [w 1 , w 2 , …, w |T|

[0032] V′ = V ∪ V e ∪{v 0 , v end}

[0033]

[0034] M(p i ) → N +

[0035] where N is the Petri net and M 0 is the initial identifier; ​​

[0036] Let \(P'\) be the set of places containing charging station information, \(T'\) be the set of transitions containing charging station information, \(Pre'\) represent the pre - incidence function containing charging station information, \(Post'\) represent the post - incidence function containing charging station information, \(S\) be the distance label vector, and \(W\) be the congestion level label vector;

[0037] Let \(P\) be the set of places, and \(P\) e be the set of places with electrical attributes; Let \(T\) be the set of transitions, and \(T\) e be the set of transitions with electrical attributes;

[0038] Let \(s\) |T| represent the distance label; Let \(w\) |T| represent the congestion condition of the current road section corresponding to the transition;

[0039] Let \(V\) be the set of nodes of the road network topology graph, \(V'\) be the set of nodes of the extended road network topology graph, and \(V\) e be the charging station location information, \(v\) 0 be the user's current location information, and \(v\) end be the target location information;

[0040] Let \(E'\) be the set of edges of the extended road network topology graph; \(v\) and \(v'\) represent two unequal nodes in the set of nodes of the extended road network topology graph, \(s\) is the actual distance between the nodes, \(w\) is the congestion coefficient of the road section, \(0.1\) represents the smooth road condition, \(0.2\) represents the relatively congested road condition, \(0.3\) represents the congested road condition, and \(\mathbb{R}^+\) represents the set of positive real numbers;

[0041] Let \(M\) be the marking vector; \(p\) i be the charging station traffic attribute library; \(M(p\) i ) represents the \(p\) - th element of the marking vector \(M\), \(M(p\) i ) represents the first element of the marking vector \(M\) corresponding to the place with charging station traffic attributes, \(M(p\) 1 ) represents the last element of the marking vector \(M\) corresponding to the place with charging station traffic attributes, \(N\) |V′| be a positive natural number; \(M(p\) + ) represents the first element of the place with electrical attributes, e1 ) represents the last element of the place with electrical attributes.

[0042] Furthermore, the depth - first search method includes the following steps:

[0043] Step 4.1: Define the node \(v\) h =(M, h, F, path);

[0044] where \(M\) represents the current marking, \(h\) represents the search depth, \(F\) represents the cumulative cost of the current marking, and \(path\) represents the sequence of transitions from the initial state to the current state;​

[0045] Define the reachable charging station node v e =(M ei , min path ) ∈ M e ;

[0046] where M ei represents the i-th reachable charging station identifier, and min path represents the minimum cost transition sequence from the initial identifier M 0 to M ei , M e is the set of reachable charging station nodes, and ∈ means belongs to;

[0047] Step 4.2. Define the initial node M 0 is the initial identifier, represents the empty set, and initialize the node set the set of reachable charging station nodes Step 4.3. If the node set enters Step 4.4, otherwise, enter Step 4.9;

[0048] Step 4.4. Obtain the first node in the node set If the identifier M = M ei and h < R, where R is the remaining mileage of the vehicle, then enter Step 4.5, otherwise enter Step 4.6;

[0049] Step 4.5. Query whether there exists the i-th reachable charging station identifier M e in the set of reachable charging station nodes M ei , if it exists, then enter Step 4.6, otherwise enter Step 4.7;

[0050] Step 4.6. Judge whether Z represents the comprehensive cost vector. If so, update the minimum cost min path of the reachable charging station node to path, otherwise remove the current node from the node set and return to Step 4.3;

[0051] Step 4.7. Add the newly reached M ei and its transition sequence min path , obtain the reachable charging station node v e =(M ei , min path ), add v e to the set of reachable charging station nodes M e , remove v hi from the node set V h and return to Step 4.3;

[0052] Step 4.8. For a node Starting from the current identifier M, calculate the next reachable identifier M′ through the state transition function;

[0053] Update the node The next node of the node

[0054] where path′ is the transition sequence reaching the current node, h′ = S · y path′ is the search depth of the transition sequence corresponding to the current node, M 0 [path′>M′, F′ = Z · y path′ is the cumulative cost corresponding to the current node, y path′ represents the transition vector of the transition sequence path′, update the node set and remove the visited nodes Return to Step 4.3;

[0055] Step 4.9. Output the set of reachable charging station nodes and the identifier set M e , where M ei ∈M e ;

[0056] The ILP method is as follows:

[0057] min F · y σ

[0058]

[0059] where F represents the cumulative cost of the current identifier, y σ is the vector corresponding to the transition sequence, N n represents the set of n - dimensional elements all being natural numbers N, M end represents the termination identifier, M ei represents the current identifier, C is the incidence matrix; s.t. means the objective function is subject to these constraints or makes these constraints hold; min means minimizing the objective function.

[0060] Furthermore, the evaluation function is:

[0061]

[0062] where O is the evaluation function, Z · y σ is the cumulative cost of the transition sequence, Z represents the comprehensive cost vector, |T C | is the dimension of the comprehensive cost vector Z, z i = w i · s i , z iis the i-th element in the comprehensive cost vector, where i is an intermediate variable, and w i is the congestion coefficient of the road section that is the i-th element in the comprehensive cost vector, and s i is the actual distance between nodes that is the i-th element in the comprehensive cost vector, and y σ is the vector corresponding to the transition sequence, c is the remaining charging piles in the charging station, and b = 1.

[0063] Second, the present invention provides a new energy vehicle charging guidance system based on a controlled Petri net, including:

[0064] An extended road network topology map acquisition module, configured to construct an extended road network topology map according to the surrounding map information of the current position of the new energy vehicle and the user side and grid side information; the user side and grid side information includes charging station capacity information and traffic condition information;

[0065] A Petri net model construction module, configured to construct a Petri net model including charging station information by adding electrical attributes to the charging station nodes according to the extended road network topology map;

[0066] A controlled Petri net obtaining module, configured to add a Petri net structure controller to the Petri net model including charging station information according to the charging station capacity information and traffic condition information to obtain a controlled Petri net model;

[0067] An optimal transition sequence obtaining module, configured to perform optimal search for distance, road condition and cost on the reachable space of the controlled Petri net model by using the depth-first search method and the ILP method to obtain an optimal transition sequence;

[0068] An optimal transition sequence output module, configured to screen the optimal transition sequence by using an evaluation function in the optimal transition sequence. The optimal transition sequence is the optimal path of the new energy vehicle from the start marking through the optimal charging station marking set to the end marking, and output the path in the road network nodes of the extended road network topology map of the optimal transition sequence to guide the new energy vehicle to the charging station.

[0069] Third, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the new energy vehicle charging guidance method based on a controlled Petri net as described above.

[0070] Fourth, the present invention provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, it implements the new energy vehicle charging guidance method based on a controlled Petri net as described above.

[0071] Compared with the prior art, the present invention has the following beneficial technical effects:

[0072] A new energy vehicle charging guidance method based on a controlled Petri net proposed by the present invention first constructs a road network model containing user-side and grid-side information and converts it into a Petri net model, simplifying the difficulty of path planning in a continuous space; on this basis, a Petri net structure controller is added according to the charging station capacity information and road condition information, and this controller can realize the constraint of the system state, thereby eliminating the relevant states where the charging station is full or the route is impassable. Solve the problems of poor versatility and long solution time in the prior art. Based on the basic concept of Internet +, according to related technologies such as intelligent transportation and mobile Internet, a controlled Petri net is constructed by integrating real-time traffic condition information, charging station location information and capacity information, vehicle information, and itinerary information, and a combined algorithm of depth-first search and ILP (Integer Linear Programming) method is used to search the reachable space of the controlled Petri net model. Finally, the best charging and travel plan is provided for the user's vehicle. When the vehicle runs out of energy during the journey, it can efficiently plan the charging station by itself and minimize the mobile loss as much as possible.

[0073] Furthermore, for the charging guidance problem, through an identity search method based on depth-first search, search for the identities related to the charging stations reachable by the remaining mileage of the vehicle, and provide a cost evaluation method for comparing and saving the optimal sequence of the transition sequences between the identities. This evaluation method comprehensively considers the driving distance and road congestion conditions, improving the user's driving experience; further using the ILP method to calculate the minimum cost sequence between the reachable charging station-related identities and the termination identity. The sequence obtained by the depth-first search method and the sequence obtained by the ILP method are spliced, and a transition sequence evaluation method for the charging path is provided to evaluate the transition sequence of the charging path, thereby outputting the optimal transition sequence. This evaluation method further takes into account the remaining capacity of the charging station, providing a guarantee for the grid load balance in the area and outputting the optimal sequence. The depth-first search and ILP methods used in the above strategies are both optimal algorithms, so the present invention provides an optimal strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] The drawings described herein are for illustrative purposes only and are not intended to limit the scope of the present invention disclosure in any way. Additionally, the shapes and proportional dimensions of the components in the drawings are only schematic for helping the understanding of the present invention and do not specifically limit the shapes and proportional dimensions of the components of the present invention. In the drawings:

[0075] Figure 1Flow chart of a new energy vehicle charging guidance method based on a controlled Petri net according to the present invention.

[0076] Figure 2 Structural diagram of a new energy vehicle charging guidance system based on a controlled Petri net according to the present invention.

[0077] Figure 3 Electronic device diagram of a new energy vehicle charging guidance method based on a controlled Petri net according to the present invention.

[0078] Figure 4 Flow block diagram of a new energy vehicle charging guidance method based on a controlled Petri net provided in an embodiment of the present invention.

[0079] Figure 5 Schematic diagram of a road network topology model including user - side and grid - side related information provided in an embodiment of the present invention.

[0080] Figure 6 Schematic diagram of a controlled Petri net system after adding a control place provided in an embodiment of the present invention.

[0081] Figure 7 New energy vehicle charging guidance route map provided in an embodiment of the present invention. Detailed implementation manners

[0082] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0083] Embodiment 1

[0084] Refer to Figure 1 , a new energy vehicle charging guidance method based on a controlled Petri net, comprising the following steps:

[0085] Construct an extended road network topology map according to the surrounding map information of the current position of the new energy vehicle and the user - side and grid - side information; the user - side and grid - side information includes charging station capacity information and traffic condition information;

[0086] According to the extended road network topology map, construct a Petri net model including charging station information by adding electrical attributes to the charging station nodes;

[0087] According to the charging station capacity information and traffic condition information, add a Petri net structure controller to the Petri net model containing charging station information to obtain a controlled Petri net model;

[0088] Adopt the depth-first search method and the ILP method to perform optimal search for distance, road condition, and cost on the reachable space of the controlled Petri net model to obtain an optimal transition sequence;

[0089] In the optimal transition sequence, use an evaluation function to screen the optimal transition sequence. The optimal transition sequence is the optimal path of the new energy vehicle from the starting mark through the optimal charging station mark set to the ending mark. Output the path of the optimal transition sequence in the road network nodes of the extended road network topology map to guide the new energy vehicle to the charging station.

[0090] The method of this embodiment not only considers the current position of the new energy vehicle and the surrounding map information, but also comprehensively considers the trip information on the user side, the charging station location information on the grid side, the charging station capacity information, and the traffic condition information. This comprehensive consideration makes the charging guidance more accurate and in line with the actual needs. By constructing a Petri net model and adding a Petri net structure controller, the method can dynamically adjust the charging strategy according to the charging station capacity and traffic condition information. This helps to avoid charging station congestion, improve charging efficiency, and at the same time reduce the waiting time of new energy vehicles. Using the depth-first search algorithm can ensure that the new energy vehicle finds the nearest and most suitable charging station during driving. This helps to save driving time and energy and improve the overall driving efficiency. Using the evaluation function to screen the optimal transition sequence realizes intelligent charging and driving decisions. It helps to improve the accuracy and reliability of decisions and provides a better user experience for new energy vehicle users. Since the method of this embodiment is constructed based on a controlled Petri net, it has good flexibility and scalability. With the continuous improvement of new energy vehicle charging facilities and the change of traffic conditions, the method can be easily adjusted and optimized to adapt to the new environment and needs. By providing an efficient and convenient charging guidance service, the method of this embodiment helps to increase the popularity and usage rate of new energy vehicles, thereby promoting the rapid development of the new energy vehicle industry.

[0091] Embodiment 2

[0092] See Figure 2 , a new energy vehicle charging guidance system based on a controlled Petri net, including:

[0093] An extended road network topology map acquisition module, configured to construct an extended road network topology map according to the surrounding map information of the current position of the new energy vehicle and the information on the user side and the grid side; the information on the user side and the grid side includes the charging station capacity information and the traffic condition information;

[0094] Build a Petri net model module, which is used to build a Petri net model with charging station information by adding electrical attributes to the charging station nodes according to the extended road network topology map;

[0095] Obtain a controlled Petri net module, which is used to add a Petri net structure controller to the Petri net model with charging station information according to the charging station capacity information and traffic condition information to obtain a controlled Petri net model;

[0096] Obtain an optimal transition sequence module, which is used to perform optimal search of distance, road condition and cost on the reachable space of the controlled Petri net model by using the depth-first search method and the ILP method to obtain an optimal transition sequence;

[0097] Output the optimal transition sequence module, which is used to screen the optimal transition sequence by using an evaluation function in the optimal transition sequence. The optimal transition sequence is the optimal path of the new energy vehicle from the starting mark through the optimal charging station mark set to the ending mark, and output the path of the optimal transition sequence in the road network nodes of the extended road network topology map to guide the new energy vehicle to the charging station.

[0098] The system in this embodiment can comprehensively consider various factors such as the current position of the new energy vehicle, the user's itinerary, the location of the charging station, the charging station capacity, and the traffic condition, and quickly recommend the most suitable charging station for the new energy vehicle. Through the depth-first search algorithm, the time and resource consumption of charging decision-making are reduced. The system not only considers the selection of the charging station, but also calculates the optimal driving path from the current position to the charging station and then to the final destination. The detour and waiting time during driving are reduced, and the overall driving efficiency is improved. The system provides an intelligent charging guidance service, which can make the optimal decision according to the actual needs of the user and the current environment. The path of the optimal transition sequence output in the road network nodes of the extended road network topology map is clear and convenient for the user to understand and execute. The controlled Petri net model has good scalability and flexibility, and can adapt to changes in different scenarios and requirements. The system can adjust the parameters of the Petri net structure controller and the evaluation function according to the actual situation to adapt to changes in new charging station layouts, traffic conditions and other factors. By providing an efficient and convenient charging guidance service, the system helps to increase the popularity and usage rate of new energy vehicles. It has positive significance for promoting the development of the new energy vehicle industry and environmental protection. The system can obtain the charging station capacity information in real time and perform charging scheduling according to the actual situation on the grid side. It helps to balance the grid load, improve the grid operation efficiency, and reduce the grid congestion and fault risks.

[0099] Embodiment 3

[0100] See Figure 3, an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a new energy vehicle charging guidance method based on a controlled Petri net. Among them, the method includes the following steps:

[0101] Construct an extended road network topology map according to the surrounding map information of the current position of the new energy vehicle and the user side and grid side information; the user side and grid side information includes charging station capacity information and traffic condition information; according to the extended road network topology map, construct a Petri net model with charging station information by adding electrical attributes to the charging station nodes; according to the charging station capacity information and traffic condition information, add a Petri net structure controller to the Petri net model with charging station information to obtain a controlled Petri net model; adopt the depth-first search method and the ILP method to perform optimal search of distance, road condition and cost on the reachable space of the controlled Petri net model to obtain an optimal transition sequence; use an evaluation function to screen the optimal transition sequence in the optimal transition sequence, and the optimal transition sequence is the optimal path of the new energy vehicle from the starting mark through the optimal charging station mark set to the ending mark, and output the path of the optimal transition sequence in the road network nodes of the extended road network topology map to guide the new energy vehicle to the charging station.

[0102] Embodiment 4

[0103] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements a new energy vehicle charging guidance method based on a controlled Petri net. Among them, the method includes the following steps:

[0104] Construct an extended road network topology map according to the surrounding map information of the current position of the new energy vehicle and the user side and grid side information; the user side and grid side information includes charging station capacity information and traffic condition information; according to the extended road network topology map, construct a Petri net model with charging station information by adding electrical attributes to the charging station nodes; according to the charging station capacity information and traffic condition information, add a Petri net structure controller to the Petri net model with charging station information to obtain a controlled Petri net model; adopt the depth-first search method and the ILP method to perform optimal search of distance, road condition and cost on the reachable space of the controlled Petri net model to obtain an optimal transition sequence; use an evaluation function to screen the optimal transition sequence in the optimal transition sequence, and the optimal transition sequence is the optimal path of the new energy vehicle from the starting mark through the optimal charging station mark set to the ending mark, and output the path of the optimal transition sequence in the road network nodes of the extended road network topology map to guide the new energy vehicle to the charging station.

[0105] Embodiment 5

[0106] The present invention provides a new energy vehicle charging guidance method based on a controlled Petri net, as follows Figure 4 , and the main steps include:

[0107] Step 1: Construct a road network topology graph and add relevant information on the user side and the power grid side;

[0108] Step 2: Construct a Petri net model according to the road network topology graph;

[0109] Step 3: Add a Petri net structure controller according to the charging station capacity information and the road condition information to obtain a controlled Petri net model of the current user's vehicle;

[0110] Step 4: Adopt a depth-first search algorithm to search for the optimal charging station identifier reachable by the remaining mileage of the vehicle in the reachable space of the controlled Petri net, and obtain the identifier set M e and save the corresponding transition conversion sequence;

[0111] Step 5: Calculate the optimal transition sequence from each identifier in the identifier set M e to the termination identifier M end and save it;

[0112] Step 6: Screen the optimal transition sequence starting from the starting identifier M 0 passing through the identifier set M e and reaching the termination identifier M end . Finally, output the corresponding path of the transition sequence in the road network nodes to guide the user's vehicle to the charging station.

[0113] The specific content of the above-mentioned Step 1 is as follows:

[0114] First, obtain the surrounding map information according to the actual location of the current user (the range of the obtained map information is set according to the actual situation), abstract the intersections in the map as road network nodes, and obtain the node set V.

[0115] Abstract the roads between two intersections as the edges of two nodes to obtain the edge set E, where the edge set V and v' represent two unequal nodes in the node set, s is the actual distance (km) between the nodes, and represents a positive real number.

[0116] Construct a road network topology graph R according to the node set V and the edge set E.

[0117] Secondly, obtain relevant information on the user side and the power grid side, including:

[0118] 1) Trip information: The current location information of the user 1) v 0 and the location information of the target location 2) v end ;

[0119] 2) Charging station location information and capacity information: Location information of charging stations matching the available charging types of the user's vehicle 3) V e , where 4) V e The elements inside are the specific location information of the available charging piles on the map. It should be noted that if the location of a charging pile coincides with a road network node, for the sake of simplifying the model, this coincident node can be considered as the node where the charging pile is located. The capacity information of each charging station can be represented as a binary tuple 5)(a, b), where 6) a represents the actual number of charging piles in use, 7) b represents the maximum number of charging piles that can be used at the charging station. Then the capacity information refers to the remaining charging pile information of this charging station 8) c = b - a.

[0120] 3) Traffic condition information: The current traffic congestion situation and traffic flow situation. If it is passable, it includes three traffic condition levels: smooth, relatively congested, and congested, and congestion coefficients 9) w ∈ {0.1, 0.2, 0.3} are respectively assigned. If it is not passable, further processing is carried out in step three.

[0121] Finally, the above-mentioned relevant information on the user side and the grid side is added to the road network topology map to obtain an extended road network topology map R'; its node set V' = V ∪ V e ∪{v 0 , v end};

[0122] Edge set where w is the congestion coefficient of this road section.

[0123] The specific content of step two is as follows:

[0124] Construct a corresponding Petri net model according to the extended road network topology map R' obtained in step one.

[0125] This model is expressed as:

[0126] N = (P, T, Pre, Post, S, W)

[0127] Among them, P and T are respectively the set of places and the set of transitions. Each place p i ∈ P, and each transition t i ∈ T. Pre: P × T → N and Post: P × T → N respectively represent the pre - incidence function and the post - incidence function. This function specifies the connection method of the directed arcs in the network, and N represents natural numbers. In the Petri net, use · p = {t ∈ T|Post(p, t) = 1} and p · = {t ∈ T|Pre(p, t) = 1} to represent the input and output transitions respectively. Among them, Post(p, t) represents the value corresponding to the Post function when the place is p and the transition is t. Similarly, Pre(p, z) represents the value corresponding to the Pre function when the place is p and the transition is t.

[0128] For a transition t i ∈ T, its input places and output places are respectively denoted as · t i = {p ∈ P|Pre(p, t i ) = 1} and t i · = {p ∈ P|Post(p, t i ) = 1}.

[0129] The incidence matrix C = Post - Pre ∈ Z obtained by matrix operations of two incidence functions. Z represents integers.

[0130] Where the node v i ∈ V' is modeled as the place p i ∈ P, and the edge (v, s, v') ∈ E' is modeled as the transition t i ∈ T.

[0131] It should be noted that the actual distances corresponding to each edge in the edge set E' form the distance label vector S = [s 1 , s 2 , …, s |T| in the form of labels in the Petri net, that is, for any transition t i ∈ T, there is a unique distance label s i in S representing the actual distance information corresponding to this transition.

[0132] W = [w 1 , w 2 , …, w |T| is the congestion level label vector, where the element w i represents the congestion situation of the road section corresponding to the current transition. For any transition t i ∈ T, there is a unique congestion level label w i in W representing the actual congestion situation corresponding to this transition. Among them, the road conditions of the road section corresponding to the transition are divided into three levels: smooth, moderately congested, and congested, corresponding to the congestion coefficients w = 0.1, 0.2, 0.3 respectively.

[0133] Among them, for the Petri net N = (P, T, Pre, Post, S, W), there is a state transition formula M = M' + C · σ, where σ is a sequence of transitions, and M is the next state reachable from the current state M' through σ. It is called that y σ is the vector corresponding to the sequence of transitions, and each element represents the number of firings of the corresponding transition t i in the sequence of transitions, denoted as y(t i ) = k, indicating that the transition t i fires k times.

[0134] For the current system model (N, M 0 ), define the identifier M = [M(p 1 ), …, M(p |V′| )] T , M(p i ) → N + . Where M(p i ) represents the p i -th element of the identifier vector M. Since the number of bits of this vector is equal to the number of elements |V′| of the node set V′, the last bit is represented as M(p |V′| ). And these elements are all positive natural numbers N + .

[0135] The identifier M is used to describe the distribution of tokens in the system at the current moment. M 0 is the initial identifier of the system, that is, the initial distribution of tokens.

[0136] Charging stations often have both electrical and traffic attributes. Therefore, during the Petri net modeling process, it is necessary to construct the electrical attributes of the charging station nodes again. The specific construction method is as follows:

[0137] If the node v i ∈V e , then it is necessary to additionally establish an electrical attribute place p ei ∈P e . The electrical attribute place is connected to the charging station traffic attribute place p i through a transition. That is, for all electrical attribute places, add two electrical attribute transitions {t ej , t ej′}∈T e such that · t ej = p ei and t ej · = p i . · t ej′ = p i and t ej′ · = p ei . Where t ej represents the j-th transition with electrical attributes, and t ej′ represents the j′-th electrical attribute transition paired with t ej .

[0138] It should be noted that the electrical attribute transitions do not have traffic attributes. Therefore, there are no corresponding actual distance labels s i and congestion situation labels w i in the distance label vector S and the congestion situation label set W (that is, the corresponding element positions are empty).

[0139] At this time, a Petri net model containing charging station information is obtained:

[0140] N = (P′, T′, Pre′, Post', S, W)

[0141] where P′ = P ∪ P e , T′ = T ∪ T e , Pre′: P′ × T′ → N, Post': P′ × T′ → N. At this time, the marking is redefined where M(P ei ) represents the element of the corresponding bit of the place with electrical attributes. Since there are |P e | (the number of places with electrical attributes P e ) such places, the last bit is represented as M(p 1 ) represents the first element of the place with charging station traffic attributes in the marking vector M, M(p |V′| ) represents the last element of the place with charging station traffic attributes in the marking vector M, M(p e1 ) represents the first element of the place with electrical attributes

[0142] The specific content of Step 3 is as follows:

[0143] Obtain the charging station capacity information c, and add a record place p ri ∈ P R . The record place is a type of structure controller of the Petri net, used to constrain the firing of transitions in the Petri net. The charging station capacity information is an inherent attribute of the charging station, and the record place should also be added to the input and output transitions of the charging station electrical attribute place p ei .

[0144] If there are no idle charging piles in the charging station, that is, there is no token in the record place p ri , at this time, the input transition of the electrical attribute place p ei of the charging station place cannot be enabled, indicating that the charging station cannot charge

[0145] For the transition t ∈ · p ei , let Pre′(p ri , t) = 1, and for the transition t ∈ p ei · , let Post′(p ri , t) = 1

[0146] · p ei represents the input transition of the electrical attribute place of the charging station place, Pre′(pri When \(Pre(p, t)=1\), it means that the place is the recording place \(p\). ri When the transition is \(t\), it is the value corresponding to the \(Pre\) function.

[0147] \(p\) ei · It represents the output transition of the place of the electrical attribute of the charging station. When \(Post'(p, t)=1\), it means that the place is the recording place \(p\). ri When \(Post(p, t)=1\), it means that the place is the recording place \(p\). ri When the transition is \(t\), it is the value corresponding to the \(Post\) function.

[0148] Among them, the number of tokens in the recording place \(p\) is equal to \(c\). If \(c = 0\), then the place of the electrical attribute of the charging station cannot be entered, so as to realize the control of the electrical attributes of the charging station. At the same time, the capacity information of the charging station is also added to the Petri net system in the form of tokens, providing support for subsequent evaluation of the advantages and disadvantages of the selected charging station. ri It should be noted that the tokens in the recording place \(p\) only represent the number of charging vehicles that can be accommodated in the current charging station, that is, the capacity information of the charging station, which has nothing to do with the user's vehicle.

[0149] Obtain traffic condition information. If the current road section cannot be passed due to external factors (such as road collapse, major accidents, etc.), then by adding a control place \(p\in P\), the constraint that the current road is impassable is realized. ri The control place is a structural controller of the Petri net, which can also restrict the firing of transitions. However, the control place is an empty place and does not contain token-related information.

[0150] For a one-way road section \(V\rightarrow V'\), the corresponding Petri net model is \(p\rightarrow t\rightarrow p'\). If the middle road from intersection \(V\) to \(V'\) cannot be passed due to reasons, at this time, a control place \(p\) is added to the transition of its Petri net model. For all transitions \(t\in p\cap p'\), let \(Pre(p', t)=1\). ci \(\in P\) C After adding the recording place \(p\) and the control place \(p\) to the Petri net \(N\) containing charging station information obtained in step three, the controlled Petri net model is obtained:

[0151] The control place is a structural controller of the Petri net, which can also restrict the firing of transitions. However, the control place is an empty place and does not contain token-related information.

[0152] For a one-way road section \(V\rightarrow V'\), the corresponding Petri net model is \(p\rightarrow t\rightarrow p'\). If the middle road from intersection \(V\) to \(V'\) cannot be passed due to reasons, at this time, a control place \(p\) is added to the transition of its Petri net model. For all transitions \(t\in p\cap p'\), let \(Pre(p', t)=1\). c For all transitions \(t\in p\) · \(\cap\) · \(p'\), let \(Pre(p', t)=1\).

[0153] After adding the recording place \(p\) and the control place \(p\) to the Petri net \(N\) containing charging station information obtained in step three, the controlled Petri net model is obtained: ri and the control place \(p\) ci we get the controlled Petri net model:

[0154] \(N\) C \(=(p\) C , \(T\) C , \(Pre\) C , \(Post\)C , S, W)

[0155] where P C = P′ ∪ P R ∪ P C , T C = T′, Pre C : P C × T′ → N, Post C : P C × T′ → N.

[0156] It should be noted that the structure controller of the Petri net only adds additional directed arcs and places, and will not affect the existing transitions. Therefore, when solving the model, it will not interfere with the path planning.

[0157] Obviously, after obtaining the relevant information on the user side and the power grid side, the controlled Petri net system (N C , M 0 ) can be obtained. For the controlled Petri net system Q C = (N C , M 0 ), redefine the marking M = [M(p 1 ), …, M(p e1 ), …, M(p r1 ), …, M(p c1 ), …] T .

[0158] M(p 1 ) represents the first element of the place with the charging station traffic attribute in the marking vector M, M(p e1 ) represents the first element of the place with the electrical attribute, M(p r1 ) represents the first element of the place with the recording place, and M(p c1 ) represents the first element of the place with the control place.

[0159] Secondly, considering the user's initial position information v 0 , within the Petir net, the distribution M 0 of tokens in the places characterizes the user's initial position information, and the current travel state of the tokens in the Petri net characterizes the user's movement in the road network.

[0160] The specific content of the fourth step is as follows:

[0161] For the controlled Petri net system (N C , M 0 ) obtained in the third step, the calculation can be performed from the initial state M 0All reachable states starting from. The directed graph composed of these reachable states is called the reachability graph RG, and the extension of RG follows the state transition formula of Petri nets.

[0162] Here, the Petri net state transition formula M = M'+C·σ is reviewed again. According to the state transition formula, each state transition corresponds to a transition sequence σ, denoted as M'[σ>M.

[0163] Generally speaking, M' represents the state of the vehicle in the system without movement, and M represents the new state of the vehicle in the system after moving along the corresponding path (transition sequence).

[0164] Obviously, by exploring the reachable space, the movement of the vehicle in the actual system can be simulated. And since Petri nets themselves can extend states with binary operations, this greatly improves the efficiency of state space expansion.

[0165] During the actual use of new energy vehicles, if the remaining energy of the vehicle is not enough to support the journey to the destination, certain travel risks will occur. To overcome the problem of insufficient energy, the charging guidance strategy should be automatically activated when the remaining energy is not enough to reach the destination. And to reduce unnecessary movement losses, a charging station on the way should be found as much as possible to charge when going to the destination. The remaining mileage R of the vehicle is used to represent the remaining energy, and the remaining mileage information can be obtained through on-vehicle systems, OBD (On-Board Diagnostics) devices, and vehicle networking platform data, etc.

[0166] For an actual vehicle system, consider its extended Petri net system (N C , M 0 ), where N C = (P C , T C , Pre C , Post C , S, W), and expand the reachability graph RG of the current system.

[0167] Since the corresponding transition sequence during state transition is known, the charging stations reachable by the remaining driving mileage from the current location can be calculated through the distance label vector S and the congestion level label vector W.

[0168] Define the driving cost F of the new energy vehicle in the reachability graph RG as F = Z·y σ , where represents the comprehensive cost vector, whose dimension is equal to the number of elements |T C | of the transition set T C . The i-th element z i in the vector = w i·s i such that M 0 [σ > M e , M e is the token generated by the transition t ∈ · p ei entering the charging station place p ei . w i and s i represent the i-th elements of the congestion level label vector W and the distance label vector S respectively.

[0169] Using the depth-first algorithm, perform token screening on the reachability graph RG, and set the search depth h = S · y σ such that M 0 [σ > M.

[0170] Only explore the tokens related to the charging stations within the depth range of h < R, so as to exclude the tokens related to the charging stations that are unreachable by the remaining mileage.

[0171] It should be noted that starting from M 0 , if there are multiple transition sequences that can reach the token M e , only keep the one with the minimum cost among them.

[0172] Specifically, the process of performing token search using the depth-first algorithm is as follows:

[0173] 4.1) Define the node v h = (M, h, F, path), where M represents the current token, h represents the corresponding search depth, F represents the cumulative cost of the current token, and path represents the transition sequence from the initial state to the current state. Define the reachable charging station node v e = (M ei , min pdth ) ∈ M e , where M ei represents the i-th reachable charging station token, and min path represents the minimum cost transition sequence from the initial token M 0 to M ei , that is, the one with the minimum F. M e is the set of reachable charging station nodes;

[0174] 4.2) Define the initial node Initialize the node set Reachable charging station node set

[0175] 4.3) If the node set enters step 4.4). Otherwise, enter step 4.9);

[0176] 4.4) Obtain the first node within the node set If the identifier M = M ei and h < R, then proceed to step 4.5), otherwise proceed to step 4.6);

[0177] 4.5) Query whether there is an M e within M ei . If it exists, then proceed to step 4.6). Otherwise, proceed to step 4.7);

[0178] 4.6) Determine whether where represents the transition vector corresponding to the minimum cost sequence min path . If so, update the minimum cost min of the reachable charging station node path = path. Otherwise, remove the current node from the node set and return to step 4.3);

[0179] 4.7) Add the newly arrived M ei and its transition sequence min path , that is, obtain the reachable charging station node v e =(M ei , min path ). Add v e to the reachable charging station node set M e . Remove from the node set V h , and return to step 4.3). (Note: For the current identifier, it can be determined whether there is If it exists, the current identifier is the charging station identifier M ei , otherwise it is not the charging station identifier.);

[0180] 4.8) For the node starting from the current identifier M, calculate the next reachable identifier M' through the state transition function (note: during this process, there may be multiple next reachable identifiers for the current identifier, and all identifiers need to be calculated). Update the next node of the node where path' is the transition sequence to reach the current node, h' = S·y path′ is the search depth of the transition sequence corresponding to the current node, such that M 0 [path'>M′, F′ = Z·y path′ is the cumulative cost corresponding to the current node, where y path′ represents the transition vector of the transition sequence path'. Update the node set and remove the visited nodes Return to step 4.3);

[0181] 4.9) Output reachable charging station node set And the identification set M e , where M ei ∈M e .

[0182] The specific content of the fifth step is as follows:

[0183] For the reachable identification set obtained in the fourth step Use ILP programming to find the optimal conversion sequence from the current identification M ei to the termination identification M end . Evaluate the quality of the sequence through F = Z·y σ . Obviously, the sequence with the minimum F is the optimal sequence.

[0184] Consider the target location information v of the user end , and represent the location information after the user moves to the target location through the target location distribution M of the token in the place within the Petri net end .

[0185] The above ILP can be expressed as equation (1)

[0186] min F·y σ

[0187]

[0188] where y σ is an n-dimensional vector, so each element of each dimension is a natural number N, expressed as y ∈ N n .

[0189] For all reachable charging station nodes v e =(M ei , path) ∈ M e the shortest path can be calculated, that is, finally, the ILP(1) needs to be run |M e | times.

[0190] For the transition sequence σ obtained by each calculation, there are associated nodes where is the associated set, which is used to save the shortest transition sequence from the reachable charging station to the user's target location.

[0191] The specific content of the sixth step is as follows:

[0192] The reachable charging station node set M e obtained in the fourth step and the associated node set obtained in the fifth step can be found that there is a certain relationship between their elements.

[0193] Taking the reachable charging station node v e =(Mei , (path) ∈ M e and associated nodes as an example.

[0194] Obviously, among the reachable charging station nodes v e in, M 0 [path > M e .

[0195] Among the associated nodes in, M e [σ > M end .

[0196] That is, from the starting mark M 0 to the ending mark M end there exists M 0 [path > M e [σ > M end .

[0197] Obviously, there is a sequence in the reachable space such that

[0198] When the token departs from the mark M 0 and reaches the ending mark M via the transition sequence end , then it must pass through the charging station-related mark M e on the way. Call the set of all constituted transition sequences the feasible solution set ∑.

[0199] Review the problem: It is necessary to provide the best charging and travel plan for the user's vehicle. When the vehicle runs out of energy during the journey, it can efficiently plan the charging station by itself and minimize the movement loss as much as possible. Obviously, the obtained set M e and together provide support for the feasible solution, but it is still necessary to find the path with the minimum loss. At this time, it is necessary to evaluate the cost of all the above-obtained transition sequences , and the evaluation method combines the cumulative cost F = Z · y of the transition sequence σ , where z i = w i · s i , and the remaining charging piles situation c of the charging station.

[0200] Considering that during the generation process of the charging guidance strategy, giving priority to going to the charging station with more remaining charging piles can not only provide relatively stable charging services for users, but also balance the power consumption of the regional power grid to a certain extent. Therefore, in the evaluation function, let the remaining charging piles number c of the charging station account for a certain proportion, and design a new evaluation function where b = 1 (which can be set according to the actual situation).

[0201] For the optimal transition sequence obtained by screening Through the correspondence between the transition set T and the edge set E = {(v, v')|v ∈ V, v' ∈ V, v ≠ v'}, it is possible to restore the transitions in to the corresponding connection relationships between nodes, that is, output in the form of a road network. In this way, it can be further restored to the travel plan guidance at the road level. Finally, the user's vehicle is guided on the map. It should be noted again that in actual use of this method, the distance to the destination and the remaining mileage are first judged. If it is unreachable, this algorithm will be activated, and then the charging guidance for the user's vehicle will be carried out.

[0202] The present embodiment will be further described with reference to the accompanying drawings:

[0203] Step 1: Construct a road network topology map and add relevant information on the user side and the power grid side;

[0204] First, according to the actual location of the user, obtain the surrounding map information. Abstract all intersections as road network nodes to obtain the node set V = {v 1 , v 2 , …, v 19}. The roads between two intersections are abstracted as the edge set

[0205] Secondly, obtain relevant information on the user side and the power grid side:

[0206] 1) Trip information: Obtain the current location information of the user and the location information of the destination point and abstract them as nodes v 0 and v end respectively;

[0207] 2) Charging station location information and capacity information: Obtain the location information of the charging stations available for the user's vehicle and abstract it as the node set V e , where V e = {v e1 , v e2 , v e3 , v e4}. The capacity information corresponding to each charging station node is: v e1 : (5, 5), v e2 : (3, 10), v e3 : (4, 5), v e4 : (2, 3).

[0208] 3) Traffic condition information: Obtain the road traffic conditions between the current nodes and assign congestion coefficients according to the congestion status. In this traffic condition, there is no impassable section.

[0209] Such asFigure 5 , add the above-mentioned user-side and grid-side related information to the road network topology map to obtain an extended road network topology map R′, whose node set V′ = V ∪ V e ∪{v 0 , v end}, edge set where w is the congestion coefficient of this road section. Each edge is respectively:

[0210] (v end , 0.465, 0.1, v 1 ), (v 1 , 0.279, 0.1, v 2 ), (v 2 , 0.595, 0.2, v 3 ), (v 3 , 0.670, 0.1, v 4 ), (v 4 , 0.781, 0.1, v 5 ), (v 5 , 0.205, 0.1, v 6 ), (v 1 , 1.000, 0.1, v 14 ), (v 3 , 0.372, 0.3, v e1 ), (v e1 , 0.521, 0.1, v 12 ), (v 12 , 0.558, 0.1, v 15 ), (v e1 , 0.670, 0.1, v 9 ), (v 9 , 0.744, 0.1, v 10 ), (v 10 , 0.334, 0.1, v 11 ), (v 7 , 0.242, 0.v, v 8 ), (v 12 , 0.074, 0.1, v 13 ), (v 13 , 0.558, 0.2, v 0 ), (v 0 , 0.744, 0.3, ve 3 ), (v 14 , 0.670, 0.2, v 15 ), (v 15 , 0.298, 0.1, v e4 ), (v e4 , 0.335, 0.1, v16 ), (v 13 , 0.117, 0.1, v e2 ), (v 4 , 0.651, 0.1, v 9 ), (v 9 , 0.465, 0.1, v 0 ), (v 0 , 0.558, 0.1, v 16 ), (v 5 , 0.372, 0.1, v 7 ), (v 7 , 0.335, 0.2, v 10 ), (v 10 , 0.465, 0.1, v e3 ), (v e1 , 0.410, 0.1, v 12 ), (v e1 , 0.372, 0.1, v 12 ).

[0211] It should be noted that all the roads involved in this map are two-way roads and have the same congestion situation. Therefore, in the corresponding edge set of its road network graph, all edge elements (v, s, w, v') are equivalent to (v', s, w, v). Due to space limitations, only the relevant elements of (v, s, w, v') are given here.

[0212] Step 2: Construct a Petri net model based on this road network topology graph;

[0213] For the road network topology graph R' constructed in the above Step 1, its corresponding Petri net model graph can be constructed. For the Petri net model N = (P, T, Pre, Post, S, W), the place set P corresponds to the node set V'. That is, for any node v ∈ V', a corresponding place p ∈ P is constructed to correspond to it; the transition set T corresponds to the edge set E'. That is, for any edge (v, s, w, v') ∈ E', there is a unique transition t ∈ T corresponding to it. It should be noted that for any edge (v, s, w, v') ∈ E', the actual distance s and the congestion situation w corresponding to the edge are respectively saved in the distance label vector S and the congestion level label vector W. For any edge (v, s, w, v'), if its corresponding transition is t, let Post(v', t) = 1 and Pre(v, t) = 1. Obviously, after inputting the incidence matrix and the post-incidence matrix for all the edges in the transition set, the pre-incidence matrix Post: P × T → N and the post-incidence matrix Pre: P × T → N can be obtained.

[0214] Step 3: Add a Petri net structure controller according to the charging station capacity information and road condition information to obtain the controlled Petri net model of the current user's vehicle;

[0215] For all the charging station nodes obtained in Step 2, add the charging station capacity information c. The charging station capacity information is stored as the number of tokens in the record place p ri ∈P R . For the places formed by all the nodes related to the charging stations, add record places. The number of tokens contained in the places is the remaining number of charging piles c of the charging station. For example, for the place Let Pre(p r1 , t) = 1, where t ∈ · p e1 , Let Post(p r1 , t) = 1, where t ∈ p e1 · . Since the place p e1 corresponds to the node v e1 , the capacity information is (5, 5), that is, the current actual number of charging piles in use is 5, the maximum number of charging piles available for charging is 5, and the remaining number of charging piles is c = 0. Therefore, the number of tokens contained in the current place p e1 is 0. For the embodiments, the charging station capacity information of all the charging station nodes is added as follows:

[0216] For the place p e2 , Let Pre(p r2 , t) = 1, where t ∈ · p e2 , Let Post(p r2 , t) = 1, where t ∈ p e2 · . The number of tokens is c = 7;

[0217] For the place p e3 , Let Pre(p r3 , t) = 1, where t ∈ · p e3 , Let Post(p r3 , t) = 1, where t ∈ p e3 · . The number of tokens is c = 1;

[0218] For the place p e4 , Let Pre(p r4 , t) = 1, where t ∈ · p e4 , Let Post(p r4 , t) = 1, where t ∈ p e4 · . The number of tokens is c = 1;

[0219] Finally, the set of record places $P$ is obtained R $=\{p$ r1 , …, $p$ r4 \}$. Since the current road does not contain impassable sections, the set of control places

[0220] After constructing the above control places, the controlled Petri net model $N$ can be obtained C $=(P$ C , $T$ C , $Pre$ C , $Post$ C , $S, W)$, where $P$ C $=P'\cup P$ R $\cup P$ C , $T$ C $=T'$, $Pre$ C : $P$ C $\times T'\to N$, $Post$ C : $P$ C $\times T'\to N$.

[0221] Step 4: Use the depth-first search algorithm to search for the optimal charging station identifier reachable by the remaining mileage of the vehicle in the reachable space of the controlled Petri net, and obtain the identifier set and save the corresponding transition sequence;

[0222] In this embodiment, the remaining driving mileage of the vehicle is 1.3 km, that is, the search depth is $R = 1.2$.

[0223] For the controlled Petri net system $Q$ C $=(N$ C , $M$ 0 ) obtained in Step 3, there is an identifier $M = [M(p$ 0 ), …, $M(p$ 16 ), $M(p$ end ), $M(p$ e1 ), …, $M(p$ r1 ), …] T .

[0224] For the initial identifier $M$ 0 , since the user's starting position is the road network node $v$ 0 , it is easy to know that $M$ 0 $=[M(p$ 0 ), …, $M(p$ 16 ), $M(p$ end ), $M(p$ e1 ), …, $M(p$ r1 ), …] T , where $M(p$ 0) = 1, M(p r1 ) = 0, M(p r2 ) = 7, M(p r3 ) = 1, M(p r4 ) = 1.

[0225] The remaining bits are all 0. At this time, an initial identifier can be uniquely determined. Therefore, there is an initial node

[0226] Starting from the initial node and performing identifier search according to the depth-first algorithm. Finally, the reachable charging station node set and the reachable charging station identifier set M e = {M e2 , M e3 , M e4}, where: v e2 = {M e2 , t 46 t 42 t e4}, M e2 = p e2 + 6p T2 + p r3 + p r4 , v e3 = {M e3 , t 38 t 27 t 39 t e5}, M e3 = p e3 + 7p r2 + p r4 , v e4 = {M e4 , t 51 t 54 t e8}, M e4 = p e4 + 7p r2 + p r3 .

[0227] Since there is no idle charging pile at the charging station v e1 , the remaining charging station capacity is 0, and the token in the corresponding record place p r1 is 0. Therefore, a control place is formed, making the token unable to enter the place p e1 representing the charging station, so that there will be no corresponding identifier M e1 .

[0228] For each node, the transition sequence with the minimum cost F = Z·y σ is retained. Taking the node v e3 as an example, starting from the initial identifier M0 Departure to the relevant signs of the charging station M e3 , that is, Token moves from place p 17 to place p e3 , there are multiple paths.

[0229] Taking sequence one: t 47 t e5 and sequence two: t 38 t 27 t 39 t e5 as an example, calculate the corresponding costs for all transition sequences, and we have:

[0230] F 1 = 0.744×0.3 + 0 = 0.223

[0231] F 2 = 0.465×0.1 + 0.744×0.1 + 0.465×0.1 = 0.167

[0232] After calculating through the cost F = Z·y σ , obviously sequence two t 38 t 27 t 39 t e5 has a smaller cost. Therefore, sequence one will not be retained as the shortest transition sequence during the iterative calculation process.

[0233] Step Five: Calculate the optimal transition sequences from each sign in the sign set M e to the termination sign M end and save them;

[0234] Since the user's destination location v end is known, from Figure 6 it can be seen that v end is modeled as place p 18 , and finally the token (user vehicle) must stay at the destination location p 18 .

[0235] Therefore, the system termination sign M end = p 18 + 7p r2 + p r3 + p r4 .

[0236] For the termination sign M end , the sign set M e = {M e2 , M e3 , M e4}, calculate the minimum sequence t from M e2 to M end respectively through ILP(1)e3 t 41 t 44 t 55 t 34 t 24 t 2 ,M e3 to M end The minimum sequence t e6 t 40 t 18 t 14 t 10 t 8 t 6 t 4 t 2 ,M e4 to M end The minimum sequence t e7 t 50 t 34 t 24 t 2 。

[0237] Therefore, there is an associated set wherein

[0238] Step Six: Screen the optimal transition sequence that starts from the starting identifier M 0 and passes through the identifier set M e to reach the termination identifier M end . Finally, output the corresponding path of the transition sequence in the road network nodes to guide the user's vehicle to the charging station.

[0239] For what is obtained in Step Four and what is obtained in Step Five , the transition sequences can be spliced so that M 0 [path>M e [σ>M end 。

[0240] The splicing result is as follows:

[0241] M 0 [t 46 t 42 t e4 >M e2 [t e3 t 41 t 44 t 55 t 34 t 24 t 2 >M end

[0242] M 0 [t 38 t27 t 39 t e5 >M e3 [t e6 t 40 t 18 t 14 t 10 t 8 t 6 t 4 t 2 >M end

[0243] M 0 [t 51 t 54 t e8 >M e2 [t e7 t 50 t 34 t 24 t 2 >M end

[0244] There is a feasible solution set

[0245] Among them,

[0246] According to the screening cost function Perform optimal cost screening on the above sequence, where

[0247] It is easy to know

[0248] Therefore, in the road network map, the driving route of the user vehicle should be v 0 →v 16 →v e4 →v 15 →v 14 →v 1 →v end . Such as Figure 7 , finally, it can be restored to the route in the map to guide the charging of the user vehicle.

[0249] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0250] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device produce means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0251] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0252] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are performed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks. Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent substitutions can still be made to the specific embodiments of the present invention, and any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered by the protection scope of the present invention.

Claims

1. A new energy vehicle charging guidance method based on a controlled Petri net, characterized in that: The following steps are involved: Construct an expanded road network topology map based on the surrounding map information of the current location of the new energy vehicle and the user-side and grid-side information; User-side and grid-side information includes charging station capacity information and traffic conditions information; According to the extended road network topology, a Petri net model containing charging station information is constructed by adding electrical attributes to charging station nodes; According to the charging station capacity information and traffic condition information, a Petri net structure controller is added to the Petri net model containing the charging station information to obtain a controlled Petri net model; The depth-first search method and ILP method are used to search the reachable space of the controlled Petri net model for optimal distance, road condition and cost, and the optimal conversion sequence is obtained. In the optimal conversion sequence, the evaluation function is used to screen the optimal transition sequence. The optimal transition sequence is the optimal path for new energy vehicles from the starting mark through the optimal charging station mark set to the ending mark. The path of the optimal transition sequence in the road network node of the extended road network topology map is output to guide the new energy vehicles to the charging station.

2. According to claim 1, a new energy vehicle charging guidance method based on a controlled Petri net is characterized in that: The construction of the extended road network topology map based on the surrounding map information of the current location of the new energy vehicle and the user side and grid side information is specifically as follows: Obtain surrounding map information based on the current location of the new energy vehicle, use the surrounding map information to build a road network topology map, obtain user-side and grid-side information, and add user-side and grid-side information to the road network topology map to build an extended road network topology map; When using the surrounding map information to construct a road network topology map, the intersections in the surrounding map information are abstracted as road network nodes, and the roads between the two intersections are abstracted as the edges of the two nodes to obtain the road network topology map.

3. A new energy vehicle charging guidance method based on controlled Petri net according to claim 2, characterized in that: The user-side and grid-side information also includes travel information and charging station location information; The trip information includes the current location information of the new energy vehicle and the location information of the target location; The charging station location information and charging station capacity information include the charging station location information matching the new energy vehicle charging type, and the charging station capacity information includes the remaining charging pile information of the charging station. Each charging station capacity information is represented by a binary group, which includes the number of charging piles actually used and the maximum number of charging piles that can be used by the charging station. The traffic condition information includes not allowing passage and allowing passage, and allowing passage includes three road condition levels: smooth, relatively congested, and congested; When adding user-side and grid-side information to the road network topology to construct an extended road network topology, the current location information of the new energy vehicle is abstracted as the starting node, the target location information is abstracted as the ending node, the charging station location information and the charging station capacity information are abstracted as a charging station node set, and the road traffic conditions between the current nodes are assigned congestion coefficients according to the traffic conditions information to obtain the extended road network topology.

4. A new energy vehicle charging guidance method based on controlled Petri net according to claim 1, characterized in that: The depth-first search and ILP method are used to perform optimal search of distance, road conditions and cost on the reachable space of the controlled Petri net model to obtain the optimal conversion sequence, which is specifically: Use the depth-first search algorithm to search for charging station identifiers that are reachable by new energy vehicles with a remaining mileage to obtain a charging station identifier set, and evaluate the cost of the charging station identifier set to obtain the optimal transition sequence of the charging station identifier set; The termination mark is used to represent the final destination of the new energy vehicle. The minimum cost sequence of the charging station mark set and the termination mark is calculated by the ILP method. The optimal transition sequence of the charging station mark set and the minimum cost sequence of the charging station mark set and the termination mark are concatenated to obtain the optimal conversion sequence.

5. A new energy vehicle charging guidance method based on controlled Petri net according to claim 4, characterized in that: The Petri net model containing the charging station information is: (N,M0) N=(P′,T′,Pre′,Post′,S,W) P′=P∪P e T′=T∪T e Pre′: P′×T′→N Post′: P′×T′→N S=[s1,s2,…,s |T| ] In=[in1,in2,…,in |T| ] V′=V∪V e ∪{v0,v end } M(p i )→N + Where N is the Petri net, M0 is the initial identifier; P′ is the set of places containing charging station information, T′ is the set of transitions containing charging station information, Pre′ represents the pre-association function containing charging station information, Post′ represents the post-association function containing charging station information, S is the distance label vector, and W is the congestion level label vector; P is the set of places, P e is the electrical property library; T is the transition set, T e For electrical property changes; s |T| Indicates the distance label; w |T| Indicates the congestion situation of the road section corresponding to the current transition; V is the node set of the road network topology graph, V′ is the node set of the extended road network topology graph, V e is the location information of the charging station, v0 is the user's current location information, and v end The target location information; E′ is the edge set of the extended road network topology graph; v and v′ represent two unequal nodes in the node set of the extended road network topology graph, s is the actual distance between the nodes, w is the congestion coefficient of the road section, 0.1 represents smooth road conditions, 0.2 represents relatively congested road conditions, and 0.3 represents congested road conditions. represents a positive real number; M is the identification vector; p i is the charging station traffic attribute library; M(p i ) represents the pth identity vector M i The element of the first position, M(p1) represents the first element of the identification vector M with the traffic attribute library of the charging station, M(p |V′| ) represents the last element of the identification vector M with the traffic attribute library of the charging station, N + is a positive natural number; M(p e1 ) represents the first element of the electrical property library, Represents the last element of the electrical property library.

6. A new energy vehicle charging guidance method based on controlled Petri net according to claim 5, characterized in that: The depth-first search method comprises the following steps: Step 4.1, define node v h =(M c ,h,F,path); Among them, M c represents the current mark, h represents the search depth, F represents the cumulative cost of the current mark, and path represents the transition sequence from the initial state to the current state; Define the reachable charging station node v e =(M ei , min path )∈M b ; Among them, M ei represents the identifier of the i-th accessible charging station, min path Indicates that from the initial mark M0 to M ei The minimum cost transition sequence, M b is the set of reachable charging station nodes, ∈ indicates belonging to; Step 4.2: Define the initial node M0 is the initial identifier, Represents an empty set, initializing the node set Reachable charging station node set Step 4.3: If the node set Go to step 4.4, otherwise, go to step 4.9; Step 4.4: Obtain the first node v in the node set hi =(M c , h, F, path). If the identifier M c = M ei and h < R, where R is the remaining mileage of the vehicle, then proceed to Step 4.5; otherwise, proceed to Step 4.6; Step 4.5: Query the reachable charging station node set M b Is there an i-th accessible charging station identifier M in ei , if it exists, go to step 4.6, otherwise go to step 4.7; Step 4.6: Determine whether Z represents the comprehensive cost vector, is the minimum cost transition sequence min path If so, update the minimum cost transition sequence min of the reachable charging station node path =path, otherwise remove the current node from the node set And return to step 4.3; Step 4.7, add new arrival M ei and its transition sequence min path , get the reachable charging station node v e =(M ei ,min path ), and v e Add to the reachable charging station node set M b , v hi From the node set V h Remove it and return to step 4.3; Step 4.8: For node v hi =(M c ,h,F,path), from the current mark M c Start, calculate the next reachable marker M' through the state transition function; Update Node The next node of the node Where path' is the transition sequence to the current node, h'=S·y path' is the search depth of the transition sequence corresponding to the current node, M0[path'>M', F'=Z·y path' is the cumulative cost corresponding to the current node, y path′ Represents the transition vector of the transition sequence path', updating the node set And remove the visited nodes Return to step 4.3; Step 4.9: Output the reachable charging station node set M b Accessible charging station logo set M e , where M ei ∈M e ; The ILP method is: minF y σ s.t.M end =M ei +C·y σ and σ ∈N n Among them, F represents the cumulative cost of the current mark, y σ is the vector corresponding to the transition sequence, N n Indicates that the elements of n-dimension are all natural numbers N, M end Indicates the termination mark, M ei represents the identifier of the i-th accessible charging station, C is the association matrix; st means that the objective function is subject to these constraints or makes these constraints hold; min means minimizing the objective function.

7. A new energy vehicle charging guidance method based on controlled Petri net according to claim 6, characterized in that: The evaluation function is: Among them, O is the evaluation function, Z·y σ is the cumulative cost of the transition sequence, Z represents the comprehensive cost vector, |T C | is the dimension of the comprehensive cost vector Z, z i =w i ·s i , z i is the i-th element in the comprehensive cost vector, i is the intermediate variable, wi is the congestion coefficient of the road section of the i-th element in the comprehensive cost vector, si is the actual distance between the nodes of the i-th element in the comprehensive cost vector, and y σ is the vector corresponding to the transition sequence, c is the remaining charging piles at the charging station, and b=1.

8. A new energy vehicle charging guidance system based on a controlled Petri net, characterized in that: include: A module for obtaining an extended road network topology map is used to construct an extended road network topology map based on the surrounding map information of the current location of the new energy vehicle and the user side and power grid side information; User-side and grid-side information includes charging station capacity information and traffic conditions information; A Petri net model building module is used to build a Petri net model containing charging station information by adding electrical attributes to charging station nodes according to the expanded road network topology diagram; A controlled Petri net module is obtained, which is used to add a Petri net structure controller to a Petri net model containing charging station information according to the charging station capacity information and traffic road condition information, so as to obtain a controlled Petri net model; The module for obtaining the optimal conversion sequence is used to use the depth-first search method and the ILP method to perform the optimal search of the distance, road condition and cost of the reachable space of the controlled Petri net model to obtain the optimal conversion sequence; The module outputs the optimal transition sequence, which is used to select the optimal transition sequence in the optimal conversion sequence using the evaluation function. The optimal transition sequence is the optimal path for the new energy vehicle from the starting mark through the optimal charging station mark set to the ending mark. The path of the optimal transition sequence in the road network node of the extended road network topology map is output to guide the new energy vehicle to the charging station.

9. An electronic device, comprising 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 for guiding charging of a new energy vehicle based on a controlled Petri net as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements a new energy vehicle charging guidance method based on a controlled Petri net as described in any one of claims 1 to 7.

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