Electric vehicle charging path planning method, system and equipment based on dynamic road condition

Abstract

The invention discloses an electric vehicle charging path planning method, system and equipment based on dynamic road conditions, and the method comprises the steps: firstly obtaining traffic road and traffic flow information, determining the comprehensive road impedance of a road, then obtaining the related information of a charging station, and calculating the related cost during charging; on the basis of the comprehensive road impedance and related cost of the road, with the aim of minimizing the road network impedance and the charging cost, path planning is conducted through a Dijkstra optimization algorithm till the electric vehicle arrives at a charging station; by determining the comprehensive road impedance, path planning can adapt to real-time changes of road conditions, the charging cost is fully considered, economical and efficient charging selection is provided for electric vehicle users, meanwhile, a path planning algorithm based on dynamic nature of traffic information and multiple factors of the users is established, and the path planning efficiency is improved. The economical efficiency of the path and the planning efficiency are ensured, and the adaptability and the flexibility of the path planning of the electric vehicle can be improved.

Description

Technical Field

[0001] The present invention relates to a path planning method, and belongs to the technical field of electric vehicles, and in particular to a method, system and device for planning a charging path for an electric vehicle based on dynamic road conditions. Background Art

[0002] In recent years, the electric vehicle market has developed rapidly due to the increase in environmental awareness and the promotion of relevant policies; consumers' acceptance of electric vehicles has continued to increase, and their ownership has been on the rise. However, the range of electric vehicles is relatively limited, which requires users to consider charging during driving, so charging route planning becomes crucial.

[0003] The modern transportation network is composed of various types of roads, such as expressways, urban trunk roads and branch roads. The traffic volume and driving speed of these roads are changing at any time, especially during rush hour, when there is severe congestion. At the same time, the distribution of charging stations is uneven, for example, there are more charging stations in urban centers and developed areas, but fewer in suburbs and remote areas. The service efficiency of charging stations is affected by many factors such as the number of charging piles and queuing conditions. When planning a reasonable charging route, these factors need to be considered comprehensively to balance the efficiency of the use of charging stations. However, the existing route planning technology usually only considers the impact of the charging distance when planning the charging route, which is somewhat one-sided, resulting in poor adaptability and flexibility of electric vehicle route planning. Summary of the invention

[0004] The purpose of the present invention is to overcome the above-mentioned defects and problems existing in the prior art and to provide a method, system and device for planning a charging path for an electric vehicle based on dynamic road conditions that can flexibly adapt to dynamic road conditions.

[0005] To achieve the above objectives, the technical solution of the present invention is: a method for planning a charging path for an electric vehicle based on dynamic road conditions, comprising:

[0006] S1. Obtain traffic road information and traffic flow information, and determine the comprehensive road impedance of the road based on dynamic road conditions;

[0007] S2. Obtain relevant information of the charging station and calculate the relevant cost of charging the vehicle at the charging station; the relevant cost includes time cost and charging price cost;

[0008] S3. When the charging demand of an electric vehicle is triggered, based on the comprehensive road impedance of the road and the relevant cost of charging at the charging station, the Dijkstra optimization algorithm is used for path planning with the goal of minimizing the road network impedance and charging cost until the electric vehicle reaches the charging station.

[0009] The step S1 specifically includes:

[0010] S11, obtaining traffic road information and establishing a traffic road network topology structure; the traffic road information includes road type, traffic volume, road network node distance, and charging station location;

[0011] The expression of the traffic network topology is as follows:

[0012]

[0013] Among them: R represents the traffic network topology, N represents the set of network nodes, V represents the set of directed arcs in the network, Z represents the set of road levels in the network, ij =1 means that the road section ij is the main road, z ij =0 means that the road section ij is a secondary road, T represents the time series set, and U represents the traffic flow u of each road in period t i，j，t A set of L represents the distance between the nodes of the road network. ij The set of, S represents the charging station set;

[0014] The distance between the nodes of the road network is l ij The distance matrix A formed ij The expression is as follows:

[0015]

[0016] Where: l ij Represents the distance from vertex i to vertex j in the distance matrix;

[0017] S12, based on the traffic flow information, a speed-flow model is introduced to calculate the road section driving speed; the traffic flow information includes vehicle speed, zero flow speed, road capacity, road flow, and road parameters;

[0018] The expression of the road section driving speed is as follows:

[0019]

[0020] Where: v ij (t) is the speed of the vehicle on the road ij at time t, v ij-m is the zero flow velocity of road ij, C ij is the traffic capacity of road ij, q ij (t) is the flow rate of road ij at time t, β is the road parameter;

[0021]

[0022] Among them: a, b, n are the coefficients of the main road or secondary road;

[0023] S13, based on the speed of the vehicle on the road ij at time t, calculate the travel time t of the vehicle on the road ij at time t ij (t) and the unit energy consumption e of the vehicle on road ij at time t ij (t), and their expressions are as follows:

[0024]

[0025]

[0026] S14, based on the unit energy consumption e of the vehicle on the road ij at time t ij (t), calculate the energy consumption w of the vehicle traveling on road ij at time t ij (t), which is expressed as follows:

[0027] w ij (t) = l ij ·e ij (t);

[0028] S15, based on the travel time t of the vehicle on road ij at time t ij (t) and the energy consumption w of the vehicle traveling on road ij at time t ij (t), calculate the comprehensive road impedance I of the road at time t jj (t);

[0029] The expression of the road resistance matrix I is as follows:

[0030]

[0031] Where: When i=j, it means the same road network node, I ij =0; when ij is not connected, it means that the road section does not exist, I ij =∞; when ij is connected, I ij That is, the comprehensive road impedance of road ij, which is determined by the road time cost t ij and road energy cost w ij composition;

[0032] The comprehensive road impedance model I of the road at time t is ij The expression of (t) is as follows:

[0033] I ij (t) = θ·t ij (t)+γ·w ij (t);

[0034] Among them: θ, γ are the unit time cost and unit electricity price of electric vehicles respectively.

[0035] The step S2 specifically includes:

[0036] S21, the time cost includes the time required for charging T charge，s，e The waiting time T wait，s ;

[0037] The expression of the charging time is as follows:

[0038]

[0039] Where: E s，e The total amount of electric energy required to charge vehicle e, P is the charging power of the charging pile, and η is the charging efficiency of the charging pile;

[0040] Considering the battery life, assuming that the vehicle ends charging when the battery capacity reaches 90%, then E s，e The expression is as follows:

[0041]

[0042] Where: Q is the battery capacity of the electric vehicle; is the energy consumption of the electric vehicle from the starting point to the charging station; x ij is a variable between 0 and 1. ij When it is 1, it means that the electric vehicle passes through road ij, otherwise it means that it does not pass through road ij;

[0043] Assuming that the process of vehicles arriving at each charging station follows a Poisson distribution, the number of charging demands for services at the charging station per hour is taken as parameter λ, then the waiting time T is wait，s The expression is as follows:

[0044]

[0045]

[0046] Where: C is the number of charging piles in the charging station, P 0 is the probability that there are no customers at the charging station, μ is the number of electric vehicles that each charging pile can serve in unit time, and ρ is the service intensity of the charging pile;

[0047] S22, the charging price cost C s That is, the price required for charging, which is expressed as follows:

[0048] C s =C all ·T charge，s，e ;

[0049] C all =C electronic +C serve ;

[0050] Where: C all is the charging unit price of the charging station at time t, C electronic is the unit price of electricity, C serve The service fee per unit time;

[0051] Based on the queuing rate, a dynamic service fee pricing model is formulated to optimize the utilization of charging stations. Its expression is as follows:

[0052]

[0053] Where: K 1 , K 2 are all price control factors, ζ(t) = λ / c represents the queuing rate of the charging station at time t, C BS Basic service fee;

[0054] S23. Based on the time cost and charging price cost, calculate and obtain the relevant cost C of the charging station station , which is expressed as follows:

[0055] C station =θ·(T charge，s，e +T wait，s )+C s ;

[0056] Where: θ is the unit time cost.

[0057] The step S3 specifically includes:

[0058] S31. When the electric vehicle is driving, the remaining battery power is Q t (i) When it is lower than the initial set threshold, the charging demand is triggered, and its expression is as follows:

[0059] Q t (i)≤ε·Q;

[0060] Where: ε is the proportionality coefficient, Q is the battery capacity of the electric vehicle;

[0061] S32. When charging demand is triggered, the Dijkstra algorithm based on dynamic road conditions is used for path planning, with the goal of minimizing the comprehensive cost. The objective function is as follows:

[0062] min F=αC station +γC road，s ;

[0063] Where: α and γ are the weights of comprehensive road impedance and charging station related cost impedance respectively; C road，s is the minimum road network impedance from the starting point to the charging station, that is, the minimum value of the sum of the comprehensive road impedances of all the driving roads from the starting point to the charging station;

[0064] S33. Select a suitable charging station based on the objective function, change the destination to the charging station, and then plan the driving route through the Dijkstra optimization algorithm based on dynamic road conditions to go to the charging station to complete charging.

[0065] The step S33 specifically includes:

[0066] S331, each road network node N in the traffic road network i Denoted as the pair value {D i , p i}; where: D i Indicates that from the starting point N 0 To the road network node N i The minimum path resistance; p i Indicates the path with the minimum road resistance, the road network node N i and record the road network node closest to each charging station as the charging station node of the charging station;

[0067] S332, record the marked node set as X, record the unmarked node set as Y, and initialize X = {N 0},Y={N 1 , N 2 ,......};

[0068] Among them: For N 0 , the starting point N 0 The minimum path resistance D to the target node 0 Set to zero; set the previous node p of the road network node Ni in the shortest path from the starting point to the target point 0 , set to an empty set; set the minimum path resistance D of all nodes in Y i is infinite; the node N where the current vehicle is located u Marked as N u =N 0 , the other nodes are unmarked nodes N j ;

[0069] S333, check the marked node N u To its directly connected unlabeled node N j The road resistance and update D j The updated expression is as follows:

[0070] D j =min{D j , D u +I uj};

[0071] Where: D j Starting point N 0To the unmarked node N j The minimum path resistance, D u Starting point N 0 To the node N where the current vehicle is located u The minimum resistance, I uj N u To N j The comprehensive road impedance;

[0072] S334, never marked node N j Select D j The node corresponding to the minimum value is denoted by N k ; N k Mark as marked, then update the set X = {X∪N k}; At the same time, find the node N from the set X k The previous node directly connected is denoted as p k , p k =p i Used to record the nodes passed by the path with the least road resistance to reach the destination;

[0073] S335: If all nodes directly connected to the charging station nodes within the preset range are marked, the check is completed, and then the minimum road network impedance from the vehicle to the charging station node is calculated, and the charging station is selected based on the objective function, and the path to the charging station is planned. At the same time, based on the road network node N i All p recorded in i , confirm the driving path;

[0074] If there are unmarked nodes directly connected to the charging station node within the preset range, then record N u =N k , and return to step S333 to continue checking;

[0075] S336, when the electric vehicle is on its way to the charging station and approaches the next intersection, the road resistance matrix is ​​updated according to the real-time traffic road information;

[0076] If the updated matrix is ​​consistent with the road resistance matrix calculated last time, the electric vehicle continues to drive along the predetermined driving path;

[0077] If the updated matrix is ​​inconsistent with the road resistance matrix calculated last time, the driving path is planned according to the updated road resistance matrix and step S33 is repeated until the electric vehicle reaches the charging station.

[0078] A system for planning a charging path for an electric vehicle based on dynamic road conditions, the system comprising:

[0079] A comprehensive road impedance determination module is used to obtain traffic road information and traffic flow information and determine the comprehensive road impedance of the road based on dynamic road conditions;

[0080] The charging cost confirmation module is used to obtain relevant information of the charging station and calculate the relevant cost of the vehicle when charging at the charging station; the relevant cost includes time cost and charging price cost;

[0081] The charging path planning module is used to plan the path when the charging demand of the electric vehicle is triggered. Based on the comprehensive road impedance of the road and the relevant cost of charging at the charging station, the Dijkstra optimization algorithm is used to minimize the road network impedance and charging cost, until the electric vehicle reaches the charging station.

[0082] The comprehensive road impedance determination module is used to determine the comprehensive road impedance according to the following steps:

[0083] S11, obtaining traffic road information and establishing a traffic road network topology structure; the traffic road information includes road type, traffic volume, road network node distance, and charging station location;

[0084] The expression of the traffic network topology is as follows:

[0085]

[0086] Among them: R represents the traffic network topology, N represents the set of network nodes, V represents the set of directed arcs in the network, Z represents the set of road levels in the network, ij =1 means that the road section ij is the main road, z ij =0 means that the road section ij is a secondary road, T represents the time series set, and U represents the traffic flow u of each road in period t i，j，t A set of L represents the distance between the nodes of the road network. ij A set of, S represents the charging station set;

[0087] The distance between the nodes of the road network is l ij The distance matrix A formed ij The expression is as follows:

[0088]

[0089] Where: l ij Represents the distance from vertex i to vertex j in the distance matrix;

[0090] S12, based on the traffic flow information, a speed-flow model is introduced to calculate the road section driving speed; the traffic flow information includes vehicle speed, zero flow speed, road capacity, road flow, and road parameters;

[0091] The expression of the road section driving speed is as follows:

[0092]

[0093] Where: v ij (t) is the speed of the vehicle on the road ij at time t, v ij-m is the zero flow velocity of road ij, C ij is the traffic capacity of road ij, q ij (t) is the flow rate of road ij at time t, β is the road parameter;

[0094]

[0095] Among them: a, b, n are the coefficients of the main road or secondary road;

[0096] S13, based on the speed of the vehicle on the road ij at time t, calculate the travel time t of the vehicle on the road ij at time t ij (t) and the unit energy consumption e of the vehicle on road ij at time t ij (t), and their expressions are as follows:

[0097]

[0098]

[0099] S14, based on the unit energy consumption e of the vehicle on the road ij at time t ij (t), calculate the energy consumption w of the vehicle traveling on road ij at time t ij (t), which is expressed as follows:

[0100] w ij (t) = l ij ·e ij (t);

[0101] S15, based on the travel time t of the vehicle on road ij at time t ij (t) and the energy consumption w of the vehicle traveling on road ij at time t ij (t), calculate the comprehensive road impedance I of the road at time t ij (t);

[0102] The expression of the road resistance matrix I is as follows:

[0103]

[0104] Where: When i=j, it means the same road network node, I ij =0; when ij is not connected, it means that the road section does not exist, I ij =∞; when ij is connected, I ijThat is, the comprehensive road impedance of road ij, which is determined by the road time cost t ij and road energy cost w ij composition;

[0105] The comprehensive road impedance model I of the road at time t is ij The expression of (t) is as follows:

[0106] I ij (t) = θ·t ij (t)+γ·w ij (t);

[0107] Among them: θ, γ are the unit time cost and unit electricity price of electric vehicles respectively.

[0108] The charging cost confirmation module is used to confirm the charging cost according to the following steps:

[0109] S21, the time cost includes the time required for charging T charge，s，e The waiting time T wait，s ;

[0110] The expression of the charging time is as follows:

[0111]

[0112] Where: E s，e The total amount of electric energy required to charge vehicle e, P is the charging power of the charging pile, and η is the charging efficiency of the charging pile;

[0113] Considering the battery life, assuming that the vehicle ends charging when the battery capacity reaches 90%, then E s，e The expression is as follows:

[0114]

[0115] Where: Q is the battery capacity of the electric vehicle; is the energy consumption of the electric vehicle from the starting point to the charging station; x ij is a variable between 0 and 1. ij When it is 1, it means that the electric vehicle passes through road ij, otherwise it means that it does not pass through road ij;

[0116] Assuming that the process of vehicles arriving at each charging station follows a Poisson distribution, the number of charging demands for services at the charging station per hour is taken as parameter λ, then the waiting time T is wait，s The expression is as follows:

[0117]

[0118] Where: C is the number of charging piles in the charging station, P0 is the probability that there are no customers at the charging station, μ is the number of electric vehicles that each charging pile can serve in unit time, and ρ is the service intensity of the charging pile;

[0119] S22, the charging price cost C s That is, the price required for charging, which is expressed as follows:

[0120] C s =C all ·T charge，s，e ;

[0121] C all =C electronic +C serve ;

[0122] Where: C all is the charging unit price of the charging station at time t, C electronic is the unit price of electricity, C serve The service fee per unit time;

[0123] Based on the queuing rate, a dynamic service fee pricing model is formulated to optimize the utilization of charging stations. Its expression is as follows:

[0124]

[0125] Where: K 1 , K 2 are all price control factors, ζ(t) = λ / c represents the queuing rate of the charging station at time t, C BS Basic service fee;

[0126] S23. Based on the time cost and charging price cost, calculate and obtain the relevant cost C of the charging station station , which is expressed as follows:

[0127] C station =θ·(T charge，s，e +T wait，s )+C s ;

[0128] Where: θ is the unit time cost.

[0129] The charging path planning module is used to plan the charging path according to the following steps:

[0130] S31. When the electric vehicle is driving, the remaining battery power is Q t (i) When it is lower than the initial set threshold, the charging demand is triggered, and its expression is as follows:

[0131] Q t (i)≤ε·Q;

[0132] Where: ε is the proportionality coefficient, Q is the battery capacity of the electric vehicle;

[0133] S32. When charging demand is triggered, the Dijkstra algorithm based on dynamic road conditions is used for path planning, with the goal of minimizing the comprehensive cost. The objective function is as follows:

[0134] min F=αC station +γC road，s ;

[0135] Where: α and γ are the weights of comprehensive road impedance and charging station related cost impedance respectively; C road，s is the minimum road network impedance from the starting point to the charging station, that is, the minimum value of the sum of the comprehensive road impedances of all the driving roads from the starting point to the charging station;

[0136] S33, selecting a suitable charging station based on the objective function, and changing the destination to the charging station, and then planning the driving route through the Dijkstra optimization algorithm based on dynamic road conditions to go to the charging station to complete charging;

[0137] The step S33 specifically includes:

[0138] S331, each road network node N in the traffic road network i Denoted as the pair value {D i , p i}; where: D i Indicates that from the starting point N 0 To the road network node N i The minimum path resistance; p i Indicates the path with the minimum road resistance, the road network node N i and record the road network node closest to each charging station as the charging station node of the charging station;

[0139] S332, record the marked node set as X, record the unmarked node set as Y, and initialize X = {N 0},Y={N 1 , N 2 ,......};

[0140] Among them: For N 0 , the starting point N 0 The minimum path resistance D to the target node 0 Set to zero; in the shortest path from the starting point to the target point, the road network node N i The previous node p 0 , set to an empty set; set the minimum path resistance D of all nodes in Y i is infinite; the node N where the current vehicle is located u Marked as Nu =N 0 , the other nodes are unmarked nodes N j ;

[0141] S333, check the marked node N u To its directly connected unlabeled node N j The road resistance and update D j The updated expression is as follows:

[0142] D j =min{D j , D u +I uj};

[0143] Where: D j Starting point N 0 To the unmarked node N j The minimum path resistance, D u Starting point N 0 To the node N where the current vehicle is located u The minimum resistance, I uj N u To N j The comprehensive road impedance;

[0144] S334, never marked node N j Select D j The node corresponding to the minimum value is denoted by N k ; N k Mark as marked, then update the set X = {X∪N k}; At the same time, find the node N from the set X k The previous node directly connected is denoted as p k , p k =p i Used to record the nodes passed by the path with the least road resistance to reach the destination;

[0145] S335: If all nodes directly connected to the charging station nodes within the preset range are marked, the check is completed, and then the minimum road network impedance from the vehicle to the charging station node is calculated, and the charging station is selected based on the objective function, and the path to the charging station is planned. At the same time, based on the road network node N i All p recorded in i , confirm the driving path;

[0146] If there are unmarked nodes directly connected to the charging station node within the preset range, then record N u =N k , and return to step S333 to continue checking;

[0147] S336, when the electric vehicle is on its way to the charging station and approaches the next intersection, the road resistance matrix is ​​updated according to the real-time traffic road information;

[0148] If the updated matrix is ​​consistent with the road resistance matrix calculated last time, the electric vehicle continues to drive along the predetermined driving path;

[0149] If the updated matrix is ​​inconsistent with the road resistance matrix calculated last time, the driving path is planned according to the updated road resistance matrix and step S33 is repeated until the electric vehicle reaches the charging station.

[0150] A device for planning a charging path for an electric vehicle based on dynamic road conditions, the device comprising a processor and a memory;

[0151] The memory is used to store computer program code and transmit the computer program code to the processor;

[0152] The processor is used to execute the above-mentioned electric vehicle charging path planning method based on dynamic road conditions according to the instructions in the computer program code.

[0153] Compared with the prior art, the present invention has the following beneficial effects:

[0154] In a method, system and device for planning a charging path for an electric vehicle based on dynamic road conditions, the method first obtains traffic road and traffic flow information, determines the comprehensive road impedance of the road, then obtains relevant information of the charging station, calculates the relevant cost during charging, and then when the charging demand of the electric vehicle is triggered, based on the comprehensive road impedance and the relevant cost of the road, with the goal of minimizing the road network impedance and the charging cost, adopts the Dijkstra optimization algorithm to perform path planning until the electric vehicle arrives at the charging station; when the design is applied, by determining the comprehensive road impedance, the path planning can adapt to the real-time changes of the road conditions, and fully considers the cost of charging, so as to provide economical and efficient charging options for electric vehicle users, and at the same time establishes a path planning algorithm based on the dynamic nature of traffic information and multiple factors of users, so as to ensure the economy of the path and the efficiency of planning, and help to improve the adaptability and flexibility of electric vehicle path planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0155] Figure 1 It is a flow chart of the method steps of the present invention.

[0156] Figure 2 It is a schematic diagram of the system structure of the present invention.

[0157] Figure 3 It is a schematic diagram of the device structure of the present invention.

[0158] In the figure: comprehensive road impedance determination module 1, charging cost confirmation module 2, charging path planning module 3, processor 4, memory 5, computer program code 51. DETAILED DESCRIPTION

[0159] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0160] Embodiment 1:

[0161] See also Figure 1 , a method for planning a charging path for an electric vehicle based on dynamic road conditions, comprising:

[0162] S1. Obtain traffic road information and traffic flow information, and determine the comprehensive road impedance of the road based on dynamic road conditions;

[0163] Furthermore, the step S1 specifically includes:

[0164] S11, obtaining traffic road information and establishing a traffic road network topology structure; the traffic road information includes road type, traffic volume, road network node distance, and charging station location;

[0165] The expression of the traffic network topology is as follows:

[0166]

[0167] Among them: R represents the traffic network topology, N represents the set of network nodes, V represents the set of directed arcs in the network, Z represents the set of road levels in the network, ij =1 means that the road section ij is the main road, z ij =0 means that the road section ij is a secondary road, T represents the time series set, and U represents the traffic flow u of each road in period t i，j，t A set of L represents the distance between the nodes of the road network. ij The set of, S represents the charging station set;

[0168] The distance between the nodes of the road network is l ij The distance matrix A formed ij The expression is as follows:

[0169]

[0170] Where: l ij Represents the distance from vertex i to vertex j in the distance matrix;

[0171] In this embodiment, the comprehensive road impedance includes two elements: driving energy consumption and driving time. In this process, the "speed-flow" model is introduced to calculate the speed, and the passing time and unit energy consumption of the road section are further derived based on the speed, and finally the comprehensive impedance of the road section is obtained, which is as follows:

[0172] S12, based on the traffic flow information, a speed-flow model is introduced to calculate the road section driving speed; the traffic flow information includes vehicle speed, zero flow speed, road capacity, road flow, and road parameters;

[0173] The expression of the road section driving speed is as follows:

[0174]

[0175] Where: v ij (t) is the speed of the vehicle on the road ij at time t; v ij-m is the zero flow velocity of road ij; C ij is the traffic capacity of road ij, which is related to the road grade; q ij (t) is the flow rate of road ij at time t; β is the road parameter;

[0176]

[0177] Among them: a, b, n are the coefficients of the main road or secondary road;

[0178] In this embodiment, different coefficients are assigned to main roads and secondary roads in the calculation according to different road grades. For main roads, coefficients a, b, and n are 1.726, 3.15, and 3; in contrast, coefficients a, b, and n for secondary roads are 2.076, 2.870, and 3, respectively.

[0179] S13, based on the speed of the vehicle on the road ij at time t, calculate the travel time t of the vehicle on the road ij at time t ij (t) and the unit energy consumption e of the vehicle on road ij at time t ij (t), and their expressions are as follows:

[0180]

[0181]

[0182] S14, based on the unit energy consumption e of the vehicle on the road ij at time t ij (t), calculate the energy consumption w of the vehicle traveling on road ij at time t ij (t), which is expressed as follows:

[0183] w ij (t) = lij ·e ij (t);

[0184] S15, based on the travel time t of the vehicle on road ij at time t jj (t) and the energy consumption w of the vehicle traveling on road ij at time t ij (t), calculate the comprehensive road impedance I of the road at time t ij (t);

[0185] The expression of the road resistance matrix I is as follows:

[0186]

[0187] Where: When i=j, it means the same road network node, I ij =0; when ij is not connected, it means that the road section does not exist, I ij =∞; when ij is connected, I ij That is, the comprehensive road impedance of road ij, which is determined by the road time cost t ij and road energy cost w ij composition;

[0188] The comprehensive road impedance model I of the road at time t is ij The expression of (t) is as follows:

[0189] I ij (t) = θ·t ij (t)+γ·w ij (t);

[0190] Among them: θ, γ are the unit time cost and unit electricity price of electric vehicles respectively.

[0191] S2. Obtain relevant information of the charging station and calculate the relevant cost of charging the vehicle at the charging station; the relevant cost includes time cost and charging price cost;

[0192] In this embodiment, the time cost includes queuing time and charging time. The queuing time is calculated by the M / M / c queuing theory model. The charging time is related to the initial SoC (State of Charge) of the electric vehicle and the path loss during driving. The charging cost includes electricity charges and service fees. The service fees are related to the current queuing rate of the charging station. By adjusting the service fees, the utilization rate of the charging station can be optimized and congestion can be reduced.

[0193] Furthermore, the step S2 specifically includes:

[0194] S21, the time cost includes the time required for charging T charge，s，e The waiting time T wait，s ;

[0195] The expression of the charging time is as follows:

[0196]

[0197] Where: E s，e The total amount of electric energy required to charge vehicle e, P is the charging power of the charging pile, and η is the charging efficiency of the charging pile;

[0198] Considering the battery life, assuming that the vehicle ends charging when the battery capacity reaches 90%, then E s，e The expression is as follows:

[0199]

[0200] Where: Q is the battery capacity of the electric vehicle; is the energy consumption of the electric vehicle from the starting point to the charging station; x ij is a variable between 0 and 1. ij When it is 1, it means that the electric vehicle passes through road ij, otherwise it means that it does not pass through road ij;

[0201] Assuming that the process of vehicles arriving at each charging station follows a Poisson distribution, the number of charging demands for services at the charging station per hour is taken as parameter λ, then the waiting time T is wait，s The expression is as follows:

[0202]

[0203] Where: C is the number of charging piles in the charging station; P 0 is the probability that there are no customers at the charging station; μ is the number of electric vehicles that can be served by each charging pile in unit time, that is, the reciprocal of the charging time of electric vehicles; ρ is the service intensity of the charging pile, ρ<1;

[0204] S22, the charging price cost C s That is, the price required for charging, which is expressed as follows:

[0205] C s =C all ·T charge，s，e ;

[0206] C all =C electronic +C serve ;

[0207] Where: C all is the charging unit price of the charging station at time t, C electronic is the unit price of electricity, C serve The service fee per unit time;

[0208] In this embodiment, within a region, each charging station implements a unified charging rate during the same time period. Users often choose the nearest charging station based on the driving distance to the charging station, which leads to serious congestion problems at some charging stations, while the utilization rate of some other charging stations is relatively low. In order to solve this problem, a dynamic service fee pricing model based on queuing rate is proposed, which aims to optimize the utilization rate of charging stations, reduce congestion, and improve overall efficiency by adjusting the service fee.

[0209] Based on the queuing rate, a dynamic service fee pricing model is formulated to optimize the utilization of charging stations. Its expression is as follows:

[0210]

[0211] Where: K 1 , K 2 are all price control factors, ζ(t) = λ / c represents the queuing rate of the charging station at time t, C BS Basic service fee;

[0212] S23. Based on the time cost and charging price cost, calculate and obtain the relevant cost C of the charging station station , which is expressed as follows:

[0213] C station =θ·(T charge，s，e +T wait，s )+C s ;

[0214] Where: θ is the unit time cost.

[0215] S3. When the charging demand of an electric vehicle is triggered, based on the comprehensive road impedance of the road and the relevant cost of charging at the charging station, the Dijkstra optimization algorithm is used for path planning with the goal of minimizing the road network impedance and charging cost until the electric vehicle reaches the charging station.

[0216] In this embodiment, when the charging demand of the electric vehicle is triggered, the electric vehicle starts charging navigation. First, the C from the starting point to each charging station within a certain range is calculated by the Dijkstra algorithm. road，s , with the goal of minimizing the driving impedance and charging cost; according to the optimization goal, a suitable charging station is selected and set as the destination, and then the path is planned through the Dijkstra optimization algorithm based on dynamic road conditions. During the driving process, when encountering an intersection, the road impedance matrix is ​​updated according to the real-time traffic information, and the road is replanned until the destination is reached, as follows:

[0217] Furthermore, the step S3 specifically includes:

[0218] S31. When the electric vehicle is driving, the remaining battery power is Q t (i) When it is lower than the initial set threshold, the charging demand is triggered, and its expression is as follows:

[0219] Q t (i)≤ε·Q;

[0220] Where: ε is the proportionality coefficient, which ranges from 0.2 to 0.35; Q is the battery capacity of the electric vehicle;

[0221] S32. When charging demand is triggered, the Dijkstra algorithm based on dynamic road conditions is used for path planning, with the goal of minimizing the comprehensive cost. The objective function is as follows:

[0222] min F=αC station +γC road，s ;

[0223] Where: α and γ are the weights of comprehensive road impedance and charging station related cost impedance respectively; C road，s is the minimum road network impedance from the starting point to the charging station, that is, the minimum value of the sum of the comprehensive road impedances of all the driving roads from the starting point to the charging station;

[0224] S33. Select a suitable charging station based on the objective function, change the destination to the charging station, and then plan the driving route through the Dijkstra optimization algorithm based on dynamic road conditions to go to the charging station to complete charging.

[0225] Furthermore, a suitable charging station is selected according to the optimization target, the destination is changed to the charging station, and the path planning is performed by the Dijkstra optimization algorithm based on dynamic road conditions. The specific steps are as follows:

[0226] The step S33 specifically includes:

[0227] S331, each road network node N in the traffic road network i Denoted as the pair value {D i , p i}; where: D i Indicates that from the starting point N 0 To the road network node N i The minimum path resistance; p i Indicates the path with the minimum road resistance, the road network node N i and record the road network node closest to each charging station as the charging station node of the charging station;

[0228] S332, record the marked node set as X, record the unmarked node set as Y, and initialize X = {N 0},Y={N 1, N 2 ,......};

[0229] Among them: For N 0 , the starting point N 0 The minimum path resistance D to the target node 0 Set to zero; in the shortest path from the starting point to the target point, the road network node N i The previous node p 0 , set to an empty set; set the minimum path resistance D of all nodes in Y i is infinite; the node N where the current vehicle is located u Marked as N u =N 0 , the other nodes are unmarked nodes N j ;

[0230] S333, check the marked node N u To its directly connected unlabeled node N j The road resistance and update D j The updated expression is as follows:

[0231] D j =min{D j , D u +I uj};

[0232] Where: D j Starting point N 0 To the unmarked node N j The minimum path resistance, D u Starting point N 0 To the node N where the current vehicle is located u The minimum path resistance, I uj N u To N j The comprehensive road impedance;

[0233] S334, never marked node N j Select D j The node corresponding to the minimum value is denoted by N k ; N k Mark as marked, then update the set X = {X∪N k}; At the same time, find the node N from the set X k The previous node directly connected is denoted as p k , p k =p i Used to record the nodes passed by the path with the least road resistance to reach the destination;

[0234] S335: If all nodes directly connected to the charging station nodes within the preset range are marked, the check is completed, and then the minimum road network impedance from the vehicle to the charging station node is calculated, and the charging station is selected based on the objective function, and the path to the charging station is planned. At the same time, based on the road network node N i All p recorded in i , confirm the driving path;

[0235] If there are unmarked nodes directly connected to the charging station node within the preset range, then record N u =N k , and return to step S333 to continue checking;

[0236] S336, when the electric vehicle is on its way to the charging station and approaches the next intersection, the road resistance matrix is ​​updated according to the real-time traffic road information;

[0237] If the updated matrix is ​​consistent with the road resistance matrix calculated last time, the electric vehicle continues to drive along the predetermined driving path;

[0238] If the updated matrix is ​​inconsistent with the road resistance matrix calculated last time, the driving path is planned according to the updated road resistance matrix and step S33 is repeated until the electric vehicle reaches the charging station.

[0239] Embodiment 2:

[0240] See also Figure 2 , a charging path planning system for electric vehicles based on dynamic road conditions, the system comprising:

[0241] The comprehensive road impedance determination module 1 is used to obtain traffic road information and traffic flow information, and determine the comprehensive road impedance of the road based on dynamic road conditions;

[0242] Furthermore, the comprehensive road impedance determination module 1 is used to determine the comprehensive road impedance according to the following steps:

[0243] S11, obtaining traffic road information and establishing a traffic road network topology structure; the traffic road information includes road type, traffic volume, road network node distance, and charging station location;

[0244] The expression of the traffic network topology is as follows:

[0245]

[0246] Among them: R represents the traffic network topology, N represents the set of network nodes, V represents the set of directed arcs in the network, Z represents the set of road levels in the network, ij =1 means that the road section ij is the main road, z ij=0 means that the road section ij is a secondary road, T represents the time series set, and U represents the traffic flow u of each road in period t i，j，t A set of L represents the distance between the nodes of the road network. ij The set of, S represents the charging station set;

[0247] The distance between the nodes of the road network is l ij The distance matrix A formed ij The expression is as follows:

[0248]

[0249] Where: l ij Represents the distance from vertex i to vertex j in the distance matrix;

[0250] S12, based on the traffic flow information, a speed-flow model is introduced to calculate the road section driving speed; the traffic flow information includes vehicle speed, zero flow speed, road capacity, road flow, and road parameters;

[0251] The expression of the road section driving speed is as follows:

[0252]

[0253] Where: v ij (t) is the speed of the vehicle on the road ij at time t, v ij-m is the zero flow velocity of road ij, C ij is the traffic capacity of road ij, q ij (t) is the flow rate of road ij at time t, β is the road parameter;

[0254]

[0255] Among them: a, b, n are the coefficients of the main road or secondary road;

[0256] S13, based on the speed of the vehicle on the road ij at time t, calculate the travel time t of the vehicle on the road ij at time t ij (t) and the unit energy consumption e of the vehicle on road ij at time t ij (t), and their expressions are as follows:

[0257]

[0258] S14, based on the unit energy consumption e of the vehicle on the road ij at time t ij (t), calculate the energy consumption w of the vehicle traveling on road ij at time t ij (t), which is expressed as follows:

[0259] w ij (t) = lij ·e ij (t);

[0260] S15, based on the travel time t of the vehicle on road ij at time t ij (t) and the energy consumption w of the vehicle traveling on road ij at time t ij (t), calculate the comprehensive road impedance I of the road at time t ij (t);

[0261] The expression of the road resistance matrix I is as follows:

[0262]

[0263] Where: When i=j, it means the same road network node, I ij =0; when ij is not connected, it means that the road section does not exist, I ij =∞; when ij is connected, I jj That is, the comprehensive road impedance of road ij, which is determined by the road time cost t ij and road energy cost w ij composition;

[0264] The comprehensive road impedance model I of the road at time t is ij The expression of (t) is as follows:

[0265] I ij (t) = θ·t ij (t)+γ·w ij (t);

[0266] Among them: θ, γ are the unit time cost and unit electricity price of electric vehicles respectively.

[0267] Charging cost confirmation module 2 is used to obtain relevant information of the charging station and calculate the relevant cost of the vehicle when charging at the charging station; the relevant cost includes time cost and charging price cost;

[0268] Furthermore, the charging cost confirmation module 2 is used to confirm the charging cost according to the following steps:

[0269] S21, the time cost includes the time required for charging T charge，s，e The waiting time T wait，s ;

[0270] The expression of the charging time is as follows:

[0271]

[0272] Where: E s，e The total amount of electric energy required to charge vehicle e, P is the charging power of the charging pile, and η is the charging efficiency of the charging pile;

[0273] Considering the battery life, assuming that the vehicle ends charging when the battery capacity reaches 90%, then E s，e The expression is as follows:

[0274]

[0275] Where: Q is the battery capacity of the electric vehicle; is the energy consumption of the electric vehicle from the starting point to the charging station; x ij is a variable between 0 and 1. ij When it is 1, it means that the electric vehicle passes through road ij, otherwise it means that it does not pass through road ij;

[0276] Assuming that the process of vehicles arriving at each charging station follows a Poisson distribution, the number of charging demands for services at the charging station per hour is taken as parameter λ, then the waiting time T is wait，s The expression is as follows:

[0277]

[0278] Where: C is the number of charging piles in the charging station, P 0 is the probability that there are no customers at the charging station, μ is the number of electric vehicles that each charging pile can serve in unit time, and ρ is the service intensity of the charging pile;

[0279] S22, the charging price cost C s That is, the price required for charging, which is expressed as follows:

[0280] C s =C all ·T charge，s，e ;

[0281] C all =C electronic +C serve ;

[0282] Where: C all is the charging unit price of the charging station at time t, C electronic is the unit price of electricity, C serve The service fee per unit time;

[0283] Based on the queuing rate, a dynamic service fee pricing model is formulated to optimize the utilization of charging stations. Its expression is as follows:

[0284]

[0285] Where: K 1 , K 2are all price control factors, ζ(t) = λ / c represents the queuing rate of the charging station at time t, C BS Basic service fee;

[0286] S23. Based on the time cost and charging price cost, calculate and obtain the relevant cost C of the charging station station , which is expressed as follows:

[0287] C station =θ·(T charge，s，e +T wait，s )+C s ;

[0288] Where: θ is the unit time cost.

[0289] The charging path planning module 3 is used to plan the path when the charging demand of the electric vehicle is triggered, based on the comprehensive road impedance of the road and the relevant cost of charging at the charging station, with the goal of minimizing the road network impedance and charging cost, using the Dijkstra optimization algorithm until the electric vehicle reaches the charging station.

[0290] Furthermore, the charging path planning module 3 is used to plan the charging path according to the following steps:

[0291] S31. When the electric vehicle is driving, the remaining battery power is Q t (i) When it is lower than the initial set threshold, the charging demand is triggered, and its expression is as follows:

[0292] Q t (i)≤ε·Q;

[0293] Where: ε is the proportionality coefficient, Q is the battery capacity of the electric vehicle;

[0294] S32. When charging demand is triggered, the Dijkstra algorithm based on dynamic road conditions is used for path planning, with the goal of minimizing the comprehensive cost. The objective function is as follows:

[0295] min F=αC station +γC road，s ;

[0296] Where: α and γ are the weights of comprehensive road impedance and charging station related cost impedance respectively; C road，s is the minimum road network impedance from the starting point to the charging station, that is, the minimum value of the sum of the comprehensive road impedances of all the driving roads from the starting point to the charging station;

[0297] S33, selecting a suitable charging station based on the objective function, and changing the destination to the charging station, and then planning the driving route through the Dijkstra optimization algorithm based on dynamic road conditions to go to the charging station to complete charging;

[0298] The step S33 specifically includes:

[0299] S331, each road network node N in the traffic road network i Denoted as the pair value {D i , p i}; where: D i Indicates that from the starting point N 0 To the road network node N i The minimum path resistance; p i Indicates the path with the minimum road resistance, the road network node N i and record the road network node closest to each charging station as the charging station node of the charging station;

[0300] S332, record the marked node set as X, record the unmarked node set as Y, and initialize X = {N 0},Y={N 1 , N 2 ,......};

[0301] Among them: For N 0 , the starting point N 0 The minimum path resistance D to the target node 0 Set to zero; in the shortest path from the starting point to the target point, the road network node N i The previous node p 0 , set to an empty set; set the minimum path resistance D of all nodes in Y i is infinite; the node N where the current vehicle is located u Marked as N u =N 0 , the other nodes are unmarked nodes N j ;

[0302] S333, check the marked node N u To its directly connected unlabeled node N j The road resistance and update D j The updated expression is as follows:

[0303] D j =min{D j , D u +I uj};

[0304] Where: D j Starting point N 0 To the unmarked node N j The minimum path resistance, D u Starting point N 0 To the node N where the current vehicle is located u The minimum resistance, Iuj N u To N j The comprehensive road impedance;

[0305] S334, never marked node N j Select D j The node corresponding to the minimum value is denoted by N k ; N k Mark as marked, then update the set X = {X∪N k}; At the same time, find the node N from the set X k The previous node directly connected is denoted as p k , p k =p i Used to record the nodes passed by the path with the least road resistance to reach the destination;

[0306] S335: If all nodes directly connected to the charging station nodes within the preset range are marked, the check is completed, and then the minimum road network impedance from the vehicle to the charging station node is calculated, and the charging station is selected based on the objective function, and the path to the charging station is planned. At the same time, based on the road network node N i All p recorded in i , confirm the driving path;

[0307] If there are unmarked nodes directly connected to the charging station node within the preset range, then record N u =N k , and return to step S333 to continue checking;

[0308] S336, when the electric vehicle is on its way to the charging station and approaches the next intersection, the road resistance matrix is ​​updated according to the real-time traffic road information;

[0309] If the updated matrix is ​​consistent with the road resistance matrix calculated last time, the electric vehicle continues to drive along the predetermined driving path;

[0310] If the updated matrix is ​​inconsistent with the road resistance matrix calculated last time, the driving path is planned according to the updated road resistance matrix and step S33 is repeated until the electric vehicle reaches the charging station.

[0311] Embodiment 3:

[0312] See also Figure 3 , a device for planning a charging path for an electric vehicle based on dynamic road conditions, the device comprising a processor 4 and a memory 5;

[0313] The memory 5 is used to store computer program code 51 and transmit the computer program code 51 to the processor 4;

[0314] The processor 4 is used to implement the method for planning a charging path for an electric vehicle based on dynamic road conditions as described in Example 1 according to the instructions in the computer program code 51 .

[0315] In this embodiment, a computer-readable storage medium is also included, in which computer-executable instructions are stored. When the computer-executable instructions are executed on a computer, the electric vehicle charging path planning method based on dynamic road conditions described in Example 1 is implemented.

[0316] Generally speaking, the computer instructions for implementing the method of the present invention may be carried in any combination of one or more computer-readable storage media. Non-transitory computer-readable storage media may include any computer-readable media, except for the signal itself that is temporarily propagating.

[0317] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EKROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0318] Computer program code for performing the operation of the present invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, SMalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages, in particular, Python suitable for neural network computing and platform frameworks based on TensorFlow, PyTorch, etc. can be used. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer or to an external computer (for example, using an Internet service provider to connect via the Internet) through any type of network, including a local area network (LAN) or a wide area network (WAN).

[0319] The above-mentioned device and non-temporary computer-readable storage medium can be found in the detailed description of a method for planning a charging path for an electric vehicle based on dynamic road conditions and its beneficial effects, which will not be repeated here.

[0320] Although the embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for planning a charging path for an electric vehicle based on dynamic road conditions, characterized in that: include: S1. Obtain traffic road information and traffic flow information, and determine the comprehensive road impedance of the road based on dynamic road conditions; S2. Obtain relevant information of the charging station and calculate the relevant cost of charging the vehicle at the charging station; the relevant cost includes time cost and charging price cost; S3. When the charging demand of an electric vehicle is triggered, based on the comprehensive road impedance of the road and the relevant cost of charging at the charging station, the Dijkstra optimization algorithm is used for path planning with the goal of minimizing the road network impedance and charging cost until the electric vehicle reaches the charging station.

2. The method for planning an electric vehicle charging path based on dynamic road conditions according to claim 1, characterized in that: The step S1 specifically includes: S11, obtaining traffic road information and establishing a traffic road network topology structure; the traffic road information includes road type, traffic volume, road network node distance, and charging station location; The expression of the traffic network topology is as follows: Among them: R represents the traffic network topology, N represents the set of network nodes, V represents the set of directed arcs in the network, Z represents the set of road levels in the network, and Z represents the set of road levels in the network. ij =1 means that the road section ij is the main road, z ij =0 means that the road section ij is a secondary road, T represents the time series set, and U represents the traffic flow u of each road in period t i,j,t A set of L represents the distance between the nodes of the road network. ij A set of, S represents the charging station set; The distance between the nodes of the road network is l ij The distance matrix A formed ij The expression is as follows: Among them: ij Represents the distance from vertex i to vertex j in the distance matrix; S12, based on the traffic flow information, a speed-flow model is introduced to calculate the road section driving speed; the traffic flow information includes vehicle speed, zero flow speed, road capacity, road flow, and road parameters; The expression of the road section driving speed is as follows: Where: v ij (t) is the speed of the vehicle on the road ij at time t, v ij-m is the zero flow velocity of road ij, C ij is the traffic capacity of road ij, q ij (t) is the flow rate of road ij at time t, β is the road parameter; Among them: a, b, n are the coefficients of the main road or secondary road; S13, based on the speed of the vehicle on the road ij at time t, calculate the travel time t of the vehicle on the road ij at time t ij (t) and the unit energy consumption e of the vehicle on road ij at time t ij (t), and their expressions are as follows: S14, based on the unit energy consumption e of the vehicle on the road ij at time t ij (t), calculate the energy consumption w of the vehicle traveling on road ij at time t ij (t), which is expressed as follows: w ij (t)=l ij ·e ij (t); S15, based on the travel time t of the vehicle on road ij at time t ij (t) and the energy consumption w of the vehicle traveling on road ij at time t ij (t), calculate the comprehensive road impedance I of the road at time t ij (t); The expression of the road resistance matrix I is as follows: Where: When i=j, it means the same road network node, I ij =0; when ij is not connected, it means that the road section does not exist, I jj =∞; when ij is connected, I ij That is, the comprehensive road impedance of road ij, which is determined by the road time cost t ij and road energy cost w ij composition; The comprehensive road impedance model I of the road at time t is ij The expression of (t) is as follows: I ij (t)=θ·t ij (t)+γ·w ij (t); Among them: θ, γ are the unit time cost and unit electricity price of electric vehicles respectively.

3. The method for planning an electric vehicle charging path based on dynamic road conditions according to claim 2, characterized in that: The step S2 specifically includes: S21, the time cost includes the time required for charging T charge，s，e The waiting time T wait，s ; The expression of the charging time is as follows: Where: E s，e The total amount of electric energy required to charge vehicle e, P is the charging power of the charging pile, and η is the charging efficiency of the charging pile; Considering the battery life, assuming that the vehicle ends charging when the battery capacity reaches 90%, then E s，e The expression is as follows: Where: Q is the battery capacity of the electric vehicle; is the energy consumption of the electric vehicle from the starting point to the charging station; x ij is a variable between 0 and 1. ij When it is 1, it means that the electric vehicle passes through road ij, otherwise it means that it does not pass through road ij; Assuming that the process of vehicles arriving at each charging station follows a Poisson distribution, the number of charging demands for services at the charging station per hour is taken as parameter λ, then the waiting time T is wait,s The expression is as follows: Where: C is the number of charging piles in the charging station, P0 is the probability that there are no customers in the charging station, μ is the number of electric vehicles that each charging pile can serve in unit time, and ρ is the service intensity of the charging pile; S22, the charging price cost C s That is, the price required for charging, which is expressed as follows: C s =C all ·T charge,s,e ; C all =C electronic +C serve ; Where: C all is the charging unit price of the charging station at time t, C electronic is the unit price of electricity, C serve The service fee per unit time; Based on the queuing rate, a dynamic service fee pricing model is formulated to optimize the utilization of charging stations. Its expression is as follows: Among them: K1, K2 are price control factors, ζ(t) = λ / c represents the queuing rate of the charging station at time t, C BS Basic service fee; S23. Based on the time cost and charging price cost, calculate and obtain the relevant cost C of the charging station station , which is expressed as follows: C station =θ·(T charge，s，e +T wait，s )+C s ; Where: θ is the unit time cost.

4. The method for planning an electric vehicle charging path based on dynamic road conditions according to claim 1, characterized in that: The step S3 specifically includes: S31. When the electric vehicle is driving, the remaining battery power is Q t (i) When it is lower than the initial set threshold, the charging demand is triggered, and its expression is as follows: Q t (i)≤ε·Q; Where: ε is the proportionality coefficient, Q is the battery capacity of the electric vehicle; S32. When charging demand is triggered, the Dijkstra algorithm based on dynamic road conditions is used for path planning, with the goal of minimizing the comprehensive cost. The objective function is as follows: minF=αC station +γC road，s ; Where: α and γ are the weights of comprehensive road impedance and charging station related cost impedance respectively; C road，s is the minimum road network impedance from the starting point to the charging station, that is, the minimum value of the sum of the comprehensive road impedances of all the driving roads from the starting point to the charging station; S33. Select a suitable charging station based on the objective function, change the destination to the charging station, and then plan the driving route through the Dijkstra optimization algorithm based on dynamic road conditions to go to the charging station to complete charging.

5. The method for planning an electric vehicle charging path based on dynamic road conditions according to claim 4 is characterized in that: The step S33 specifically includes: S331, each road network node N in the traffic road network i Denoted as the pair value {D i , p i }; where: D i Indicates the distance from starting point N0 to network node N i The minimum path resistance; p i Indicates the path with the minimum road resistance, the road network node N i and record the road network node closest to each charging station as the charging station node of the charging station; S332, record the marked node set as X, record the unmarked node set as Y, and initialize X={N0}, Y={N1, N2, ...}; Among them: for N0, the minimum road resistance D0 from the starting point N0 to the target node is set to zero; in the shortest path from the starting point to the target point, the road network node N i The previous node p0 is set to an empty set; the minimum path resistance D of all nodes in Y i is infinite; the node N where the current vehicle is located u Marked as N u =N0, other nodes are unmarked nodes N j ; S333, check the marked node N u To its directly connected unlabeled node N j The road resistance and update D j The updated expression is as follows: D j =min{D j ,D u +Iu j }; Where: D j From the starting point N0 to the unmarked node N j The minimum path resistance, D u From the starting point N0 to the node N where the current vehicle is located u The minimum resistance, I uj N u To N j The comprehensive road impedance; S334, never marked node N j Select D j The node corresponding to the minimum value is denoted by N k ; N k Mark as marked, then update the set X = {X∪N k }; At the same time, find the node N from the set X k The previous node directly connected is denoted as p k , p k =p i Used to record the nodes passed by the path with the least road resistance to reach the destination; S335: If all nodes directly connected to the charging station nodes within the preset range are marked, the check is completed, and then the minimum road network impedance from the vehicle to the charging station node is calculated, and the charging station is selected based on the objective function, and the path to the charging station is planned. At the same time, based on the road network node N i All p recorded in i , confirm the driving path; If there are unmarked nodes directly connected to the charging station node within the preset range, then record N u =N k , and return to step S333 to continue checking; S336, when the electric vehicle is on its way to the charging station and approaches the next intersection, the road resistance matrix is ​​updated according to the real-time traffic road information; If the updated matrix is ​​consistent with the road resistance matrix calculated last time, the electric vehicle continues to drive along the predetermined driving path; If the updated matrix is ​​inconsistent with the road resistance matrix calculated last time, the driving path is planned according to the updated road resistance matrix and step S33 is repeated until the electric vehicle reaches the charging station.

6. An electric vehicle charging path planning system based on dynamic road conditions, characterized in that: The system comprises: A comprehensive road impedance determination module (1) is used to obtain traffic road information and traffic flow information, and determine the comprehensive road impedance of the road based on dynamic road conditions; A charging cost confirmation module (2) is used to obtain relevant information of the charging station and calculate the relevant cost of the vehicle when charging at the charging station; the relevant cost includes time cost and charging price cost; The charging path planning module (3) is used to plan the path using the Dijkstra optimization algorithm based on the comprehensive road impedance and the relevant cost of charging at the charging station when the charging demand of the electric vehicle is triggered, with the goal of minimizing the road network impedance and the charging cost, until the electric vehicle reaches the charging station.

7. The electric vehicle charging path planning system based on dynamic road conditions according to claim 6 is characterized in that: The comprehensive road impedance determination module (1) is used to determine the comprehensive road impedance according to the following steps: S11, obtaining traffic road information and establishing a traffic road network topology structure; the traffic road information includes road type, traffic volume, road network node distance, and charging station location; The expression of the traffic network topology is as follows: Among them: R represents the traffic network topology, N represents the set of network nodes, V represents the set of directed arcs in the network, Z represents the set of road levels in the network, ij =1 means that the road section ij is the main road, z ij =0 means that the road section ij is a secondary road, T represents the time series set, and U represents the traffic flow u of each road in period t i,j,t A set of L represents the distance between the nodes of the road network. ij A set of, S represents the charging station set; The distance between the nodes of the road network is l ij The distance matrix A formed ij The expression is as follows: Among them: ij Represents the distance from vertex i to vertex j in the distance matrix; S12, based on the traffic flow information, a speed-flow model is introduced to calculate the road section driving speed; the traffic flow information includes vehicle speed, zero flow speed, road capacity, road flow, and road parameters; The expression of the road section driving speed is as follows: Where: ij (t) is the speed of the vehicle on the road ij at time t, v ij-m is the zero flow velocity of road ij, C ij is the traffic capacity of road ij, q ij (t) is the flow rate of road ij at time t, β is the road parameter; Among them: a, b, n are the coefficients of the main road or secondary road; S13, based on the speed of the vehicle on the road ij at time t, calculate the travel time t of the vehicle on the road ij at time t ij (t) and the unit energy consumption e of the vehicle on road ij at time t ij (t), and their expressions are as follows: S14, based on the unit energy consumption e of the vehicle on the road ij at time t ij (t), calculate the energy consumption w of the vehicle traveling on road ij at time t ij (t), which is expressed as follows: w ij (t)=l ij ·e ij (t); S15, based on the travel time t of the vehicle on road ij at time t ij (t) and the energy consumption w of the vehicle traveling on road ij at time t ij (t), calculate the comprehensive road impedance I of the road at time t ij (t); The expression of the road resistance matrix I is as follows: Where: When i=j, it means the same road network node, I ij =0; when ij is not connected, it means that the road section does not exist, I ij =∞; when ij is connected, I ij That is, the comprehensive road impedance of road ij, which is determined by the road time cost t ij and road energy cost w ij composition; The comprehensive road impedance model I of the road at time t is ij The expression of (t) is as follows: I ij (t)=θ·t ij (t)+γ·w ij (t); Among them: θ, γ are the unit time cost and unit electricity price of electric vehicles respectively.

8. The electric vehicle charging path planning system based on dynamic road conditions according to claim 6 is characterized in that: The charging cost confirmation module (2) is used to confirm the charging cost according to the following steps: S21, the time cost includes the time required for charging T charge,s,e The waiting time T wait,s ; The expression of the charging time is as follows: Where: E s,e The total amount of electric energy required to charge vehicle e, P is the charging power of the charging pile, and η is the charging efficiency of the charging pile; Considering the battery life, assuming that the vehicle ends charging when the battery capacity reaches 90%, then E s,e The expression is as follows: Where: Q is the battery capacity of the electric vehicle; is the energy consumption of the electric vehicle from the starting point to the charging station; x ij is a variable between 0 and 1. ij When it is 1, it means that the electric vehicle passes through road ij, otherwise it means that it does not pass through road ij; Assuming that the process of vehicles arriving at each charging station follows a Poisson distribution, the number of charging demands for services at the charging station per hour is taken as parameter λ, then the waiting time T is wait，s The expression is as follows: Where: C is the number of charging piles in the charging station, P0 is the probability that there are no customers in the charging station, μ is the number of electric vehicles that each charging pile can serve in unit time, and ρ is the service intensity of the charging pile; S22, the charging price cost C s That is, the price required for charging, which is expressed as follows: C s =C all ·T charge，s，e ; C all =C electronic +C serve ; Where: C all is the charging unit price of the charging station at time t, C electronic is the unit price of electricity, C serve The service fee per unit time; Based on the queuing rate, a dynamic service fee pricing model is formulated to optimize the utilization of charging stations. Its expression is as follows: Among them: K1, K2 are price control factors, ζ(t) = λ / c represents the queuing rate of the charging station at time t, C BS Basic service fee; S23. Based on the time cost and charging price cost, calculate and obtain the relevant cost C of the charging station station , which is expressed as follows: C station =θ·(T charge，s，e +T wait，s )+C s ; Where: θ is the unit time cost.

9. The electric vehicle charging path planning system based on dynamic road conditions according to claim 6, characterized in that: The charging path planning module (3) is used to plan the charging path according to the following steps: S31. When the electric vehicle is driving, the remaining battery power is Q t (i) When it is lower than the initial set threshold, the charging demand is triggered, and its expression is as follows: Q t (i)≤ε·Q; Where: ε is the proportionality coefficient, Q is the battery capacity of the electric vehicle; S32. When charging demand is triggered, the Dijkstra algorithm based on dynamic road conditions is used for path planning, with the goal of minimizing the comprehensive cost. The objective function is as follows: min F=αC statioin +γC road，s ; Where: α and γ are the weights of comprehensive road impedance and charging station related cost impedance respectively; C road，s is the minimum road network impedance from the starting point to the charging station, that is, the minimum value of the sum of the comprehensive road impedances of all the driving roads from the starting point to the charging station; S33, selecting a suitable charging station based on the objective function, and changing the destination to the charging station, and then planning the driving route through the Dijkstra optimization algorithm based on dynamic road conditions to go to the charging station to complete charging; The step S33 specifically includes: S331, each road network node N in the traffic road network i Denoted as the pair value {D i , p i }; where: D i Indicates the distance from starting point N0 to network node N i The minimum path resistance; p i Indicates the path with the minimum road resistance, the road network node N i and record the road network node closest to each charging station as the charging station node of the charging station; S332, record the marked node set as X, record the unmarked node set as Y, and initialize X={N0}, Y={N1, N2, ...}; Among them: for N0, the minimum road resistance D0 from the starting point N0 to the target node is set to zero; in the shortest path from the starting point to the target point, the road network node N i The previous node p0 is set to an empty set; the minimum path resistance D of all nodes in Y i is infinite; the node N where the current vehicle is located u Marked as N u =N0, other nodes are unmarked nodes N j ; S333, check the marked node N u To its directly connected unlabeled node N j The road resistance and update D j The updated expression is as follows: D j =min{D j ,D u +I uj }; Where: D j From the starting point N0 to the unmarked node N j The minimum path resistance, D u From the starting point N0 to the node N where the current vehicle is located u The minimum path resistance, I uj N u To N j The comprehensive road impedance; S334, never marked node N j Select D j The node corresponding to the minimum value is denoted by N k ; N k Mark as marked, then update the set X = {X∪N k }; At the same time, find the node N from the set X k The previous node directly connected is denoted as p k , p k =p i Used to record the nodes passed by the path with the least road resistance to reach the destination; S335: If all nodes directly connected to the charging station nodes within the preset range are marked, the check is completed, and then the minimum road network impedance from the vehicle to the charging station node is calculated, and the charging station is selected based on the objective function, and the path to the charging station is planned. At the same time, based on the road network node N i All p recorded in i , confirm the driving path; If there are unmarked nodes directly connected to the charging station node within the preset range, then record N u =N k , and return to step S333 to continue checking; S336, when the electric vehicle is on its way to the charging station and approaches the next intersection, the road resistance matrix is ​​updated according to the real-time traffic road information; If the updated matrix is ​​consistent with the road resistance matrix calculated last time, the electric vehicle continues to drive along the predetermined driving path; If the updated matrix is ​​inconsistent with the road resistance matrix calculated last time, the driving path is planned according to the updated road resistance matrix and step S33 is repeated until the electric vehicle reaches the charging station.

10. An electric vehicle charging path planning device based on dynamic road conditions, characterized in that: The device comprises a processor (4) and a memory (5); The memory (5) is used to store computer program code (51) and transmit the computer program code (51) to the processor (4); The processor (4) is used to execute the electric vehicle charging path planning method based on dynamic road conditions according to any one of claims 1 to 5 according to the instructions in the computer program code (51).

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