A method and device for simulating charging and hydrogenation load of an electric vehicle and a hydrogen fuel cell vehicle

Abstract

This invention provides a method and apparatus for simulating the charging and hydrogen refueling loads of electric vehicles and hydrogen fuel cell vehicles. To address the difficulty in characterizing the load at coupled nodes in power distribution networks and transportation networks, a mathematical model is first established, incorporating the energy system of electric vehicles and hydrogen fuel cell vehicles, the power distribution network, and the transportation network. A certain number of origin-destination (OD) pairs are randomly generated based on the transportation network topology to represent vehicle travel demand, and each traveling vehicle is assigned parameters such as initial electricity or hydrogen quantity. Secondly, a road impedance function and functions for judging vehicle charging and refueling status are established. Finally, the A*search algorithm is used to build the shortest charging path model for electric vehicles and the shortest refueling path model for hydrogen fuel cell vehicles. Users charge or refuel based on the vehicle's energy state, current location, and distance to the destination, obtaining the electrical load of the charging station and the hydrogen load of the refueling station at that time. The system assesses the arrival status of all vehicles at their destinations, and exits the system once all vehicle path planning is complete.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy system and transportation network joint planning and scheduling, and relates to a method and device for simulating the charging and hydrogen refueling load of electric vehicles and hydrogen fuel cell vehicles. Background Technology

[0002] Hydrogen energy, as a high-quality secondary energy source, shows great potential in energy transition and zero-carbon travel. Fuel cell vehicles (HFCVs) are an important application of hydrogen energy in the transportation sector. HFCVs have received widespread attention as a green transportation tool. Currently, the reasons for the limited widespread adoption of HFCVs include high purchase prices, fuel costs, maintenance costs, and incomplete hydrogen refueling infrastructure. However, the concentrated charging of electric vehicles with a high proportion of users puts a significant strain on the power grid; disordered charging loads can lead to multiple peak loads on the grid, jeopardizing grid stability. Compared to electric vehicles, HFCVs have shorter refueling times and a time delay between hydrogen production load and peak electricity load, thus not increasing the difficulty of power supply or the pressure on grid operation. With the maturation of technology and the decrease in hydrogen production costs, fuel cell vehicles, with their high energy density, zero emissions, and fast refueling speed, have broad development prospects. However, the highly random nature of fuel cell vehicle travel times makes hydrogen refueling load difficult to predict.

[0003] Currently, most methods for simulating the charging load of electric vehicles and the refueling load of hydrogen fuel cell vehicles borrow from electric vehicle load prediction techniques, using the product of traffic flow passing through the node and the charging rate coefficient. However, this load simulation method cannot handle the randomness of travel time for electric vehicles and hydrogen fuel cell vehicles, as well as changes in road impedance, resulting in an inaccurate prediction of refueling load. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention proposes a method and apparatus for simulating the charging and hydrogen refueling loads of electric vehicles and hydrogen fuel cell vehicles, thereby solving the problem of difficulty in characterizing the loads of coupled nodes in power distribution networks and transportation networks.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A method for simulating the charging and hydrogen refueling load of electric vehicles and hydrogen fuel cell vehicles specifically includes the following steps:

[0007] Step 1: Construct a load simulation framework for electric vehicles and hydrogen fuel cell vehicles that considers the coupling of power distribution networks, transportation networks, and energy systems, and model the power distribution network to provide energy to electric vehicles and hydrogen fuel cell vehicles; use an undirected graph G = [T] composed of a series of roads and intersections. N ,T L Modeling the transportation network, where TN Let T be a set of nodes. L Let N be a set of routes; where node N corresponds to an intersection and route L corresponds to a road connecting the nodes; a traffic network consisting of N nodes and M branches is represented as an adjacency matrix A.

[0008] The hydrogen production model is represented by equation (1):

[0009]

[0010] In the formula: Let k be the amount of hydrogen produced by the j-th electrolyzer during time period t; p2h p is the electro-hydrogen conversion coefficient. j,t,e The power consumed by the j-th electrolytic cell during time period t;

[0011] Hydrogen refueling stations use hydrogen storage tanks to store excess hydrogen for backup. The hydrogen storage model is shown in the following formula:

[0012]

[0013] In the formula: The hydrogen storage status of hydrogen refueling station at node j at time t; The hydrogen storage status of hydrogen refueling station at node j at time t-1; Let be the hydrogen production of the j-th electrolyzer during time period t at time t; Let the hydrogen load of the j-th hydrogen refueling station at time t be denoted as ; These represent the amount of hydrogen at the beginning of the i-th hydrogen storage tank scheduling cycle and the amount of hydrogen at the end of the scheduling cycle, respectively. These are the lower and upper limits for the hydrogen storage volume of hydrogen storage tanks, respectively.

[0014] The adjacency matrix A is represented as:

[0015]

[0016] In equation (3): row vectors correspond to branches, and column vectors correspond to nodes; if the a-th branch is from node i to node j, then A ai =1, A aj =-1, and the rest of the components are 0;

[0017] Step 2: Based on the aforementioned transportation network model, the origin-end point (OD) matrix is ​​used to characterize the travel demand model M for electric vehicles and hydrogen fuel cell vehicles. OD ;

[0018]

[0019] Where: M rs This represents the traffic flow from node r to node s.

[0020] Step 3: Based on the aforementioned demand model M OD Models for the charging and driving characteristics of electric vehicles and models for the hydrogen refueling and driving characteristics of hydrogen fuel cell vehicles were established respectively.

[0021] Step 4: Based on the electric vehicle charging and driving characteristic model and the hydrogen fuel cell vehicle refueling and driving characteristic model, and using the A*search algorithm as a foundation, real-time traffic information is incorporated to dynamically plan the shortest travel route for users.

[0022] Step 5: Based on the shortest travel route and the remaining mileage information obtained from the vehicle's current location information, determine the need for charging and hydrogen refueling by checking the electric vehicle battery status and the hydrogen fuel cell refueling status, and determine the charging and hydrogen refueling load calculation method.

[0023] Furthermore, in step 1:

[0024] The power distribution system is formed by connecting with the regional transportation network. The power distribution network is a radial network, and its power flow equations are as follows:

[0025]

[0026]

[0027]

[0028]

[0029]

[0030]

[0031]

[0032]

[0033]

[0034]

[0035] In the formula: and Let B and B0 represent the active and reactive power injected into node j at time t, respectively; B and B0 represent the distribution network node set and reference node, respectively. θ(j) and θ(j) are the set of child nodes and the set of parent nodes of node j, respectively; and These are respectively the active power and reactive power injected into the distribution network from the upper-level power grid; and These represent the active power output of the gas turbine, the load of the hydrogen refueling station, the wind power output, and the electrical load at node i at time t, respectively. and These represent the active power flow and reactive power on the line from node i to node j, respectively; v it Vi and V0 represent the voltage and reference voltage of node i at time t, respectively; vi jt Let R be the voltage at point i at time t; ij X represents the resistance between node i and node j; ij This represents the reactance between node i and node j; Indicates voltage deviation; and These represent the maximum active and reactive power transmitted by the line, respectively.

[0036] Furthermore, in step 3, each time the user's vehicle reaches a node, the algorithm can automatically adjust the previously planned path according to changes in road conditions and determine the next target node. The electric vehicle or hydrogen fuel cell vehicle is a private car with random travel needs and unfixed driving paths.

[0037] Furthermore, in step 3, the electric vehicle charging characteristics include charging station number, charging start time, charging end time, battery state of charge, power consumption per kilometer, and battery state when fully charged; wherein, the power consumption per kilometer is based on a power consumption model C for electric vehicles per unit mileage, established according to different road conditions. m The driving characteristics of electric vehicles include vehicle number, vehicle location information, driving route, and vehicle departure and arrival node numbers.

[0038]

[0039] In the formula: C evr A set of charging characteristic parameters for electric vehicles; T evN C is a set of driving characteristic parameters for electric vehicles. evh It is the charging station number; t evs t is the charging start time. evd C is the charging end time. t The battery state at time t; C m C represents the power consumption per kilometer. F The battery is fully charged; N ev For vehicle number; L evt Let R be the vehicle position at time t; evp For the driving route; O evp D is the vehicle's starting point. evs The destination of the vehicle; EV indicates electric vehicle.

[0040] Electric vehicle power consumption model C m :

[0041]

[0042] In the formula: C1, C2, and C3 represent the electricity consumption per unit mileage for electric vehicles in road grades I, II, and III, respectively; V ij The average speed of traffic on a road segment can be represented by the length of the road segment, l. ij With road travel time The calculation formula is as follows:

[0043]

[0044] Furthermore, in step 3, the vehicle hydrogen refueling characteristics include the hydrogen refueling station number, refueling time, hydrogen state, hydrogen consumption per kilometer, and hydrogen state when fully refueled; wherein the hydrogen consumption per kilometer is based on a hydrogen fuel cell vehicle unit mileage hydrogen consumption model H established according to different road conditions. m The vehicle driving characteristics include vehicle number, vehicle location information, driving path, and vehicle departure and arrival node numbers.

[0045]

[0046] In the formula: C fcr A set of characteristic parameters for hydrogen refueling of automobiles; T fcN C is a set of vehicle driving characteristic parameters; fch It is the hydrogen refueling station number; t fcs For hydrogenation time; H t The state of hydrogen at time t; H F The state of hydrogen when the tank is full; N fcv For vehicle number; L fct Let R be the vehicle position at time t; fcp For the driving route; O fcp D is the vehicle's starting point. fcs For vehicle destination; HFCV indicates hydrogen fuel cell vehicle;

[0047] Hydrogen consumption model per unit mileage for hydrogen fuel cell vehicles H m :

[0048]

[0049] In the formula: H1, H2, and H3 represent the energy consumption per unit mileage of hydrogen fuel cell vehicles for road grades I, II, and III, respectively; V ij The average speed of traffic on a road segment can be represented by the length of the road segment, l. ij With road travel time The calculation formula is as follows:

[0050]

[0051] Furthermore, in step 4, using the adjacency matrix A as the data foundation and the A*search algorithm as the basis, real-time traffic information is incorporated to dynamically plan the shortest travel route for the user. The specific steps are as follows:

[0052] 1) Obtain the real-time road resistance function and origin / end point nodes based on the traffic network adjacency matrix A, road resistance function, and OD pairs; divide all nodes in the road network into two sets S and W, respectively storing the nodes to be visited and the nodes that have been visited;

[0053] 2) Initialization: Add the starting point to the set S, find the nodes that the starting point can reach, add them to the set S, and set the starting point as the parent node;

[0054] 3) Remove the starting point from set S and put the starting point into set W;

[0055] 4) Calculate the prediction function of the child nodes surrounding the current parent node. The prediction function is calculated using the following formula:

[0056] f(n) = g(n) + h(n)

[0057] In the formula: f(n) represents the comprehensive estimated value of the node; g(n) represents the distance from the starting point to the current node; h(n) uses the Manhattan distance formula to represent the distance from the current node to the target node;

[0058] 5) Select the node with the smallest estimated value, set it as the parent node, remove the node from S, and add it to the W set;

[0059] 6) Repeat steps 4) and 5) until the target node is reached;

[0060] 7) Output the nodes in set W to obtain the car's driving path.

[0061] Furthermore, the specific calculation method for step 5 is as follows:

[0062] (1) Obtain traffic network information including road segment length, maximum traffic flow of road segment and road segment impedance, and read the travel demand of electric vehicles and hydrogen fuel cell vehicles in the traffic network, including travel time and initial location and vehicle destination, and randomly generate electric vehicle SOC and hydrogen fuel cell vehicle SOH.

[0063] (2) Based on the current location information of the electric vehicle, the remaining mileage information is obtained. The electric vehicle determines whether it needs to be charged based on its charging status. If so, the nearest charging station within the remaining battery level range is searched, and the charging path is planned using the A*search algorithm. The user charges based on the vehicle's remaining battery level, current location, and distance to the destination, thus obtaining the charging station's electrical load at that time. After charging is completed, the shortest driving path is planned based on the current road resistance. If not, the vehicle travels according to the planned path. The status of all electric vehicles in the system is assessed, and the system exits when all vehicle paths have been planned. The charging station load is calculated using the following formula:

[0064]

[0065] In the formula: Let C be the load of the charging station located at node j at time t; F The battery state when the electric vehicle is fully charged; C i,t Let A be the battery state of the i-th electric vehicle at time t; A is the traffic network matrix; C evr T evN These represent the charging characteristics and driving characteristics of electric vehicles, respectively; δ() represents the electric vehicle route optimization function, which yields the charging demand and the selected charging station information;

[0066] (3) Based on the vehicle's current location information, the remaining mileage information is obtained. The hydrogen refueling status is used to determine whether refueling is needed. If needed, the nearest hydrogen refueling station within the vehicle's remaining hydrogen range is searched, and the A*search algorithm is used to plan the refueling route. The user refuels based on the vehicle's remaining hydrogen supply, the distance between the current location and the vehicle's destination, and the hydrogen load at the refueling station is obtained. If not needed, the vehicle proceeds along the planned route. The arrival status of all vehicles in the system is assessed, and the system exits when all vehicle routes are planned. The hydrogen refueling station load is calculated using the following formula:

[0067]

[0068] In the formula: H represents the load of the hydrogen refueling station located at node j at time t; F The state of hydrogen when a hydrogen fuel cell vehicle is fully refueled; H i,t Let A be the hydrogen state of the i-th hydrogen fuel cell vehicle at time t; let A be the traffic network matrix; C be the hydrogen state of the i-th hydrogen fuel cell vehicle at time t; fcr T fcN These represent the hydrogen refueling characteristics and driving characteristics of hydrogen fuel cell vehicles, respectively; δ() represents the hydrogen fuel cell vehicle route optimization function, which yields the hydrogen refueling demand and the selected hydrogen refueling station information.

[0069] Furthermore, in step 5, if charging is required, the system searches for the nearest charging station within the vehicle's remaining battery level range and uses the A*search algorithm to plan the charging path. The user charges based on the vehicle's remaining hydrogen and the distance between the current location and the vehicle's destination, thus obtaining the charging station's electrical load at that time. If charging is not required, the vehicle travels along the planned path. The system assesses the arrival status of all vehicles at their destinations and exits when all vehicle paths have been planned.

[0070] Furthermore, in step 5, if hydrogen refueling is required, the system searches for the nearest hydrogen refueling station within the vehicle's remaining hydrogen capacity range and uses the A*search algorithm to plan the refueling route. The user refuels based on the vehicle's remaining hydrogen supply, current location, and distance to the vehicle's destination, thus obtaining the hydrogen load at the refueling station. If hydrogen refueling is not required, the vehicle proceeds along the planned route. The system assesses the arrival status of all vehicles at their destinations and exits the system once all vehicle routes have been planned.

[0071] The present invention also provides an apparatus for implementing the above-described method for simulating the charging and hydrogen refueling load of electric vehicles and hydrogen fuel cell vehicles, comprising:

[0072] Establish a framework system module: used to construct a simulation framework for the coupled electric-hydrogen load of the power distribution network, transportation network and energy system; model the transportation network using an undirected graph composed of a series of roads and intersections to obtain the transportation network model; a transportation network with N nodes and M branches is represented as an adjacency matrix A;

[0073] Transportation and Mobility Module: In the transportation network model, the origin-end point (OD) matrix is ​​used to characterize the travel demand model M for electric vehicles and hydrogen fuel cell vehicles within the transportation network. OD ;

[0074] Vehicle charging and driving module: Based on the travel demand of electric vehicles and hydrogen fuel cell vehicles, models of electric vehicle charging characteristics and driving characteristics and models of hydrogen fuel cell vehicle hydrogen refueling characteristics and driving characteristics are established respectively.

[0075] The charging path planning module: Based on the acquisition of electric vehicle charging and driving characteristic models and hydrogen fuel cell vehicle refueling and driving characteristics, it uses the A*search algorithm as a basis, incorporates real-time traffic information, and aims to minimize the time required for dynamic path planning of electric vehicles and hydrogen fuel cell vehicles.

[0076] Hydrogen demand calculation module: Based on the vehicle's current location information, it obtains the remaining mileage information, and determines whether charging or refueling is required by the charging status and refueling status, thus obtaining the charging and refueling load calculation method.

[0077] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the program to implement the steps of the above-described electric vehicle and hydrogen fuel cell vehicle charging and hydrogen refueling load simulation method.

[0078] The present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, characterized in that the computer program, when executed by a processor, implements the steps of the above-described electric vehicle and hydrogen fuel cell vehicle charging and hydrogen refueling load simulation method.

[0079] Beneficial effects:

[0080] This invention considers the charging and refueling needs of electric vehicles and hydrogen fuel cell vehicles, constructs a collaborative optimization operation model for an electric-hydrogen-transportation network coupled system, uses the A*search algorithm to determine the driving paths of electric vehicles and hydrogen fuel cell vehicles based on user travel demand, and provides charging and refueling rules for electric vehicles and hydrogen fuel cell vehicles. It also obtains a traffic flow allocation model and a power distribution network load model that take into account charging and refueling behavior, thereby improving the utilization of new energy sources and promoting the integration of comprehensive energy systems and transportation networks. Attached Figure Description

[0081] Figure 1 This is a flowchart illustrating a method for simulating the charging and hydrogen refueling load of electric vehicles and hydrogen fuel cell vehicles according to the present invention. Detailed Implementation

[0082] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0083] Specifically, such as Figure 1 As shown, the method for simulating the charging and hydrogen refueling load of electric vehicles and hydrogen fuel cell vehicles according to the present invention specifically includes the following steps:

[0084] Step 1: Model the power distribution network, transportation network, and energy system. Construct a load simulation framework for electric vehicles and hydrogen fuel cell vehicles that considers the coupling of the power distribution network, transportation network, and energy system. Use Lindistflow to model the power distribution network; use an undirected graph G = [T] composed of a series of roads and intersections. N ,T L A transportation network model is formed by modeling the transportation network, where T N Let T be a set of nodes. LLet N be a set of routes. Node N corresponds to an intersection, and route L corresponds to a road connecting the nodes. The road topology is described using a road adjacency matrix. A traffic network consisting of N nodes and M branches can be further represented as an adjacency matrix A.

[0085] In a hydrogen energy system, hydrogen refueling stations use electrolyzers to consume electricity from the upstream power grid and wind farms to produce hydrogen, which is then pressurized and stored. When a fuel cell vehicle refuels, high-pressure gaseous hydrogen from the storage tank is injected into the fuel cell vehicle via a hydrogen refueling machine. Since the refueling time is very short compared to the driving time, waiting time at refueling stations and refueling time are not considered in this invention; energy losses during the refueling process are also not considered. The hydrogen production model is represented by equation (1):

[0086]

[0087] In the formula: Let k be the amount of hydrogen produced by the j-th electrolyzer during time period t; p2h p is the electro-hydrogen conversion coefficient. j,t,e Let t be the power consumed by the j-th electrolyzer during time period t. Due to the limitations of current hydrogen production technology, the electro-hydrogen conversion coefficient is taken as 43.75 kWh / kg.

[0088] Hydrogen refueling stations use hydrogen storage tanks to store excess hydrogen for backup. The hydrogen storage model is shown in the following formula.

[0089]

[0090] In the formula: The hydrogen storage status of hydrogen refueling station at node j at time t; The hydrogen storage status of hydrogen refueling station at node j at time t-1; Let be the hydrogen production of the j-th electrolyzer during time period t at time t; Let the hydrogen load of the j-th hydrogen refueling station at time t be denoted as ; These represent the amount of hydrogen at the beginning of the i-th hydrogen storage tank scheduling cycle and the amount of hydrogen at the end of the scheduling cycle, respectively. These represent the lower and upper limits of the hydrogen storage tank's capacity.

[0091] A transportation network consisting of N nodes and M branches can be further represented as an adjacency matrix A, specifically as follows:

[0092]

[0093] In the formula: row vectors correspond to branches, and column vectors correspond to nodes. If the a-th branch is from node i to node j, then A ai =1, Aaj =-1, and the rest of the components are 0.

[0094] A power distribution system connected to the regional transportation network has a radial distribution network, and its power flow equations are as follows:

[0095]

[0096]

[0097]

[0098]

[0099]

[0100]

[0101]

[0102]

[0103]

[0104]

[0105] In the formula: and Let B and B0 represent the active and reactive power injected into node j at time t, respectively; B and B0 represent the distribution network node set and reference node, respectively. θ(j) and θ(j) are the set of child nodes and the set of parent nodes of node j, respectively; and These are respectively the active power and reactive power injected into the distribution network from the upper-level power grid; and These represent the active power output of the gas turbine, the load of the hydrogen refueling station, the wind power output, and the electrical load at node i at time t, respectively. and These represent the active power flow and reactive power on the line from node i to node j, respectively; v it Vi and V0 represent the voltage and reference voltage of node i at time t, respectively; vi jt Let R be the voltage at point i at time t; ij X represents the resistance between node i and node j; ij This represents the reactance between node i and node j; Indicates voltage deviation; and These represent the maximum active and reactive power transmitted by the line, respectively.

[0106] Step 2: Use the origin-destination (OD) matrix to characterize the travel demand model M for electric vehicles and hydrogen fuel cell vehicles. OD The departure and arrival nodes are usually work areas or residential areas, while the nodes along the way are usually intersections.

[0107] The travel demand for electric vehicles and hydrogen fuel cell vehicles is characterized using an origin-destination (OD) matrix, where (r, s) is defined as an OD pair, and r is the origin of the OD pair, s is the destination of the OD pair, and M... OD This is a set of OD pairs. The origin and destination nodes are usually work areas or residential areas, while the nodes along the way are usually intersections, etc.

[0108]

[0109] Where: M rs This represents the traffic flow from node r to node s.

[0110] Step 3: Based on the aforementioned demand model M OD Models for the charging and driving characteristics of electric vehicles and the hydrogen refueling and driving characteristics of hydrogen fuel cell vehicles were established respectively.

[0111] Electric vehicle charging characteristics include charging station number, charging start time, charging end time, battery state of charge, energy consumption per kilometer, and battery state when fully charged; among which, the energy consumption per kilometer is established based on different road conditions to create a unit mileage energy consumption model C for electric vehicles. m The driving characteristics of electric vehicles include vehicle number, vehicle location information, driving route, and vehicle departure and arrival node numbers.

[0112]

[0113] In the formula: C evr A set of charging characteristic parameters for electric vehicles; T evN C is a set of driving characteristic parameters for electric vehicles. evh It is the charging station number; t evs t is the charging start time. evd C is the charging end time. t The battery state at time t; C m C represents the power consumption per kilometer. F The battery is fully charged; N ev For vehicle number; L evt Let R be the vehicle position at time t; evp For the driving route; O evp D is the vehicle's starting point. evs The destination of the vehicle; EV indicates electric vehicle.

[0114] Electric vehicle power consumption model C m :

[0115]

[0116] In the formula: C1, C2, and C3 represent the electricity consumption per unit mileage for electric vehicles in road grades I, II, and III, respectively; V ij The average speed of traffic on a road segment can be represented by the length of the road segment, l. ij With road travel time The calculation formula is as follows:

[0117]

[0118] The hydrogen refueling characteristics of hydrogen fuel cell vehicles include refueling station number, refueling time, hydrogen state, hydrogen consumption per kilometer, and hydrogen state when fully refueled; among which, the hydrogen consumption per kilometer is modeled based on different road conditions to determine the hydrogen consumption per unit mile for hydrogen fuel cell vehicles. m The driving characteristics of hydrogen fuel cell vehicles include vehicle number, vehicle location information, driving route, and vehicle departure and arrival node numbers.

[0119]

[0120] In the formula: C fcr A set of characteristic parameters for hydrogen refueling of automobiles; T fcN C is a set of vehicle driving characteristic parameters; fch It is the hydrogen refueling station number; t fcs For hydrogenation time; H t The state of hydrogen at time t; H F The state of hydrogen when the tank is full; N fcv For vehicle number; L fct Let R be the vehicle position at time t; fcp For the driving route; O fcp D is the vehicle's starting point. fcs The destination of the vehicle; HFCV stands for hydrogen fuel cell vehicle.

[0121] Step 4: Based on the electric vehicle charging and driving characteristics model and the hydrogen fuel cell vehicle refueling and driving characteristics model, using the adjacency matrix A as the data basis and the A*search algorithm as the basis, and incorporating real-time traffic information, the algorithm dynamically plans the shortest travel route for users: that is, every time the user's vehicle reaches a node, the algorithm can automatically adjust the previously planned route according to changes in traffic conditions and determine the next target node.

[0122] 1) Obtain the real-time road resistance function and origin / end point nodes based on the traffic network adjacency matrix A, road resistance function, and OD pairs; divide all nodes in the road network into two sets S and W, respectively storing the nodes to be visited and the nodes that have been visited;

[0123] 2) Initialization: Add the starting point to the set S, find the nodes that the starting point can reach, add them to the set S, and set the starting point as the parent node;

[0124] 3) Remove the starting point from set S and put the starting point into set W;

[0125] 4) Calculate the prediction function of the child nodes surrounding the current parent node. The prediction function is calculated using the following formula:

[0126] f(n) = g(n) + h(n)

[0127] In the formula: f(n) represents the comprehensive estimated value of the node; g(n) represents the distance from the starting point to the current node; h(n) uses the Manhattan distance formula to represent the distance from the current node to the target node;

[0128] 5) Select the node with the smallest estimated value, set it as the parent node, remove the node from S, and add it to the W set;

[0129] 6) Repeat steps 4) and 5) until the target node is reached;

[0130] 7) Output the nodes in set W to obtain the car's driving path.

[0131] Step 5: Based on the shortest travel route and the remaining mileage information obtained from the vehicle's current location, and by considering the electric vehicle battery status and the hydrogen fuel cell refueling status, determine the method for calculating the refueling load when refueling is required.

[0132] (1) First, obtain the traffic network information, including road segment length, maximum traffic flow of road segment and road segment impedance. Read the travel demand of electric vehicles and hydrogen fuel cell vehicles in the traffic network, including travel time and initial location and vehicle destination. Randomly generate the SOC of electric vehicles and SOH of hydrogen fuel cell vehicles.

[0133] (2) Based on the current location information of the electric vehicle, the remaining mileage information is obtained. The electric vehicle determines whether it needs to be charged based on its charging status. If so, the nearest charging station within the remaining battery level range is searched, and the charging path is planned using the A*search algorithm. The user charges based on the vehicle's remaining battery level, current location, and distance to the destination, thus obtaining the charging station's electrical load at that time. After charging is completed, the shortest driving path is planned based on the current road resistance. If not, the vehicle travels according to the planned path. The status of all electric vehicles in the system is assessed, and the system exits when all vehicle paths have been planned. The charging station load is calculated using the following formula:

[0134]

[0135] In the formula: Let C be the load of the charging station located at node j at time t; F The battery state when the electric vehicle is fully charged; C i,t Let A be the battery state of the i-th electric vehicle at time t; A is the traffic network matrix; C evr T evN These represent the charging characteristics and driving characteristics of electric vehicles, respectively; δ() represents the electric vehicle route optimization function, which yields the charging demand and the selected charging station information;

[0136] (3) Based on the current location information of the hydrogen fuel cell vehicle, the remaining mileage information is obtained, and the need for refueling is determined by the refueling status. If refueling is required, the nearest refueling station within the vehicle's remaining hydrogen capacity is searched, and the refueling route is planned using the A*search algorithm. The user refuels based on the vehicle's remaining hydrogen supply, the distance between the current location and the vehicle's destination, and the hydrogen load at the refueling station at that time is obtained. If refueling is not required, the vehicle continues driving according to the planned route. The system assesses the arrival status of all vehicles at their destinations, and exits the system once all vehicle routes have been planned.

[0137]

[0138] In the formula: H represents the load of the hydrogen refueling station located at node j at time t; F The hydrogen state of a fuel cell vehicle when fully refueled; H i,t Let A be the hydrogen state of the i-th fuel cell vehicle at time t; let A be the traffic network matrix; C be the hydrogen state of the i-th fuel cell vehicle at time t; fcr T fcN These represent the hydrogen refueling characteristics and driving characteristics of fuel cell vehicles, respectively; δ() represents the fuel cell vehicle route optimization function, which yields the hydrogen refueling demand and the selected hydrogen refueling station information.

[0139] This invention also provides an apparatus for simulating the charging and hydrogen refueling load of electric vehicles and hydrogen fuel cell vehicles, specifically comprising:

[0140] Establish a framework system module: used to construct a simulation framework for the coupled electric-hydrogen load of the power distribution network, transportation network and energy system; model the transportation network using an undirected graph composed of a series of roads and intersections to obtain the transportation network model; a transportation network with N nodes and M branches is represented as an adjacency matrix A;

[0141] Transportation and vehicle travel module: In the transportation network model, the origin-end point (OD) matrix is ​​used to characterize the fuel cell vehicle travel demand model M of the transportation network. OD ;

[0142] Vehicle charging and driving module: Based on the travel demand of electric vehicles and hydrogen fuel cell vehicles, models of electric vehicle charging characteristics and driving characteristics and models of hydrogen fuel cell vehicle hydrogen refueling characteristics and driving characteristics are established respectively.

[0143] The charging path planning module: Based on the acquisition of electric vehicle charging and driving characteristic models and hydrogen fuel cell vehicle refueling and driving characteristics, it uses the A*search algorithm as a basis, incorporates real-time traffic information, and aims to minimize the time required for dynamic path planning of electric vehicles and hydrogen fuel cell vehicles.

[0144] Hydrogen demand calculation module: Based on the vehicle's current location information, it obtains the remaining mileage information, and determines whether charging or refueling is required by the charging status and refueling status, thus obtaining the charging and refueling load calculation method.

[0145] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above method.

[0146] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0147] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application 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. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0148] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0149] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0150] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0151] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0152] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for simulating charging and hydrogen loading of an electric vehicle and a hydrogen fuel cell vehicle, characterized by, Specifically, the steps include the following: Step 1: Construct a load simulation framework for electric vehicles and hydrogen fuel cell vehicles that considers the coupling of power distribution network, transportation network and energy system, and model the power distribution network to realize the power supply for electric vehicles and hydrogen fuel cell vehicles; use an undirected graph composed of a series of roads and intersections to model the transportation network and obtain the transportation network model; a transportation network with N nodes and M branches is represented as an adjacency matrix A; Step 2: Based on the traffic network model, the traffic origin-destination OD matrix is used to depict the travel demand model of electric vehicles and hydrogen fuel cell vehicles ; Step 3: Based on the aforementioned demand model Models for the charging and driving characteristics of electric vehicles and models for the hydrogen refueling and driving characteristics of hydrogen fuel cell vehicles were established respectively. Step 4, based on the electric vehicle charging characteristics and driving characteristics model and hydrogen fuel cell vehicle hydrogen refueling characteristics and driving characteristics model, to A based on the A* search algorithm, and real-time traffic information is integrated to dynamically plan the shortest travel path for the user Step 5: Based on the shortest travel route and the remaining mileage information obtained from the vehicle's current location, and by considering the electric vehicle battery status and the hydrogen fuel cell refueling status, determine the method for calculating the refueling load when refueling is required. This includes: (1) Obtain traffic network information including road segment length, maximum traffic flow of road segment and road segment impedance, and read the travel demand of electric vehicles and hydrogen fuel cell vehicles in the traffic network, including travel time and initial location and vehicle destination, and randomly generate electric vehicle SOC and hydrogen fuel cell vehicle SOH; (2) Based on the current location information of the electric vehicle, the remaining mileage information is obtained. The electric vehicle determines whether it needs to be charged based on its charging status. If it needs to be charged, the nearest charging station within the remaining battery level range is searched for, using A. The search algorithm plans charging routes. Users charge based on their vehicle's remaining battery power and the distance between their current location and destination, thus determining the charging station's electrical load at that moment. After charging is complete, the shortest route is planned based on current road resistance. If no further charging is needed, the vehicle follows the planned route. The system assesses the status of all electric vehicles; once all vehicle routes are planned, the system exits. The charging station load is calculated using the following formula: （8） In the formula: The load of the charging station located at node j at time t; The battery status of an electric vehicle when fully charged; Let A be the battery state of the i-th electric vehicle at time t; A is the traffic network matrix. , These refer to the charging characteristics and driving characteristics of electric vehicles, respectively. This represents the electric vehicle route optimization function, which yields charging demand and selected charging station information; (3) Obtain the remaining mileage information based on the current location information of the hydrogen fuel cell vehicle, and determine whether hydrogen refueling is needed based on the hydrogen refueling status; if needed, search for the nearest hydrogen refueling station within the remaining hydrogen status range for the vehicle, using A The search algorithm plans hydrogen refueling routes. Users refuel based on their vehicle's remaining hydrogen supply and the distance between their current location and destination, thus determining the hydrogen load at the refueling station. If no hydrogen is needed, the user continues along the planned route. The system assesses the arrival status of all hydrogen fuel cell vehicles within the system; once all vehicle routes are planned, the system exits the algorithm. The refueling station load is calculated using the following formula: （9） In the formula: The load of the hydrogen refueling station located at node j at time t; The state of hydrogen when a hydrogen fuel cell vehicle is fully refueled; Let A represent the hydrogen state of the i-th hydrogen fuel cell vehicle at time t; A is the traffic network matrix. , These are the hydrogen refueling characteristics and driving characteristics of hydrogen fuel cell vehicles, respectively. This represents the route optimization function for hydrogen fuel cell vehicles, which yields information on hydrogen refueling demand and selected hydrogen refueling stations.

2. The electric vehicle and hydrogen fuel cell vehicle charging and hydrogen loading simulation method of claim 1, wherein, In step 4, road connections are represented by an adjacency matrix A, which is expressed as follows: （1） In equation (1): row vectors correspond to branches, and column vectors correspond to nodes; if the first... The branch road is from Node to Nodes =1、 =-1, and the rest of the components are 0.

3. The electric vehicle and hydrogen fuel cell vehicle charging and hydrogen loading simulation method of claim 1, wherein, In step 3, the electric vehicle charging characteristics include charging station number, charging start time, charging end time, battery state of charge, power consumption per kilometer, and battery state when fully charged; wherein, the power consumption per kilometer is modeled based on different road conditions to determine the power consumption per unit mile of the electric vehicle. The driving characteristics of electric vehicles include vehicle number, vehicle location information, driving route, and vehicle departure and arrival node numbers. （2） In the formula: A set of charging characteristic parameters for electric vehicles; This is a set of parameters representing the driving characteristics of electric vehicles. It is the charging station number; This refers to the charging start time; This is the charging end time; for Real-time battery status; Electricity consumption per kilometer; The battery is fully charged. Number the vehicle; for Real-time vehicle location; For the driving route; The starting point of the vehicle; For vehicle destination; EV indicates electric vehicle; Electric vehicle power consumption model per unit distance : （3） In the formula: and The electricity consumption per unit mileage for electric vehicles in road grades I, II, and III are respectively. The average speed of traffic on a road segment can be expressed by the length of the road segment. With road travel time The calculation formula is as follows: （4）。 4. The electric vehicle and hydrogen fuel cell vehicle charging and hydrogen loading simulation method of claim 1, wherein, In step 3, the hydrogen refueling characteristics of the hydrogen fuel cell vehicle include the refueling station number, refueling time, hydrogen state, hydrogen consumption per kilometer, and hydrogen state when fully refueled; wherein the hydrogen consumption per kilometer is modeled based on different road conditions to determine the hydrogen consumption per unit mile of the hydrogen fuel cell vehicle. The driving characteristics of hydrogen fuel cell vehicles include vehicle number, vehicle location information, driving route, and vehicle departure and arrival node numbers. （5） In the formula: A set of hydrogen refueling characteristic parameters for hydrogen fuel cell vehicles; This is a set of driving characteristic parameters for hydrogen fuel cell vehicles. It is the hydrogen refueling station number; This refers to the hydrogenation time; for The state of hydrogen at any given moment; This refers to the hydrogen state when the tank is full. Number the vehicle; for Real-time vehicle location; For the driving route; The starting point of the vehicle; For vehicle destination; HFCV indicates hydrogen fuel cell vehicle; Hydrogen consumption per unit mile of hydrogen fuel cell vehicle model : （6） In the formula: and These are the energy consumption per unit mileage for hydrogen fuel cell vehicles of road grade I, grade II, and grade III, respectively. The average speed of traffic on a road segment can be expressed by the length of the road segment. With road travel time The calculation formula is as follows: （7）。 5. The electric vehicle and hydrogen fuel cell vehicle charging and hydrogen loading simulation method of claim 1, wherein, In step 4, based on the adjacency matrix A as data basis, and the A search algorithm, the real-time traffic information is integrated to dynamically plan the shortest travel path for the user, and the specific steps are as follows: 1) Obtain the real-time road resistance function and origin / end point nodes based on the traffic network adjacency matrix A, road resistance function, and OD pairs; divide all nodes in the road network into two sets S and W, respectively storing the nodes to be visited and the nodes that have been visited; 2) Initialization: Add the starting point to the set S, find the nodes that the starting point can reach, add them to the set S, and set the starting point as the parent node; 3) Remove the starting point from set S and add the starting point to set W; 4) Calculate the prediction function of the child nodes surrounding the current parent node. The prediction function is calculated using the following formula: In the formula: represents the comprehensive estimated value of the node; represents the distance from the starting point to the current node; represents the distance from the current node to the target node by using the Manhattan distance formula. 5) Select the node with the smallest estimated value, set it as the parent node, remove the node from S, and add it to the W set; 6) Repeat steps 4) and 5) until the target node is reached; 7) Output the nodes in set W to obtain the car's driving path.

6. The electric vehicle and hydrogen fuel cell vehicle charging and hydrogen loading simulation method of claim 1, wherein, In step 5, if charging is required, the system searches for the nearest charging station within the vehicle's remaining battery level range, using method A. The search algorithm plans the charging route. The user charges the vehicle based on the remaining battery power, the distance between the current location and the destination, and obtains the electrical load of the charging station at this time. If charging is not required, the vehicle will proceed along the planned route. The system will assess the arrival status of all vehicles at their destinations and exit once all vehicle routes have been planned.

7. The electric vehicle and hydrogen fuel cell vehicle charging and hydrogen loading simulation method of claim 1, wherein, In step 5, if hydrogen refueling is required, the system searches for the nearest hydrogen refueling station within the vehicle's remaining hydrogen capacity range, using method A. The search algorithm plans the hydrogen refueling route. The user refuels based on the remaining hydrogen in the vehicle and the distance between the current location and the destination, and obtains the hydrogen load of the hydrogen refueling station at this time. If no hydrogen refueling is needed, the user drives according to the planned route. The system assesses the arrival status of all vehicles at their destinations and exits once all vehicle routes have been planned.

8. An apparatus for simulating the charging and hydrogen refueling load of electric vehicles and hydrogen fuel cell vehicles, characterized in that, include: Establish a framework system module: used to construct a simulation framework for the coupled electric-hydrogen load of the power distribution network, transportation network and energy system; model the transportation network using an undirected graph composed of a series of roads and intersections to obtain the transportation network model; a transportation network with N nodes and M branches is represented as an adjacency matrix A; Transportation and Mobility Module: In the transportation network model, the origin-end point (OD) matrix is ​​used to characterize the travel demand model for electric vehicles and hydrogen fuel cell vehicles within the transportation network. ; Vehicle charging and driving module: Based on the travel demand of electric vehicles and hydrogen fuel cell vehicles, models of electric vehicle charging characteristics and driving characteristics and models of hydrogen fuel cell vehicle hydrogen refueling characteristics and driving characteristics are established respectively. The charging path planning module: Based on the acquisition of electric vehicle charging and driving characteristic models and hydrogen fuel cell vehicle hydrogen refueling and driving characteristics, using A... Based on the search algorithm, real-time traffic information is incorporated to minimize the time required for dynamic path planning of electric vehicles and hydrogen fuel cell vehicles. The hydrogen demand calculation module obtains remaining range information based on the vehicle's current location, determines whether charging and refueling are necessary based on charging and refueling status, and derives a charging and refueling load calculation method, including: (1) Obtain traffic network information including road segment length, maximum traffic flow of road segment and road segment impedance, and read the travel demand of electric vehicles and hydrogen fuel cell vehicles in the traffic network, including travel time and initial location and vehicle destination, and randomly generate electric vehicle SOC and hydrogen fuel cell vehicle SOH; (2) Based on the current location information of the electric vehicle, the remaining mileage information is obtained. The electric vehicle determines whether it needs to be charged based on its charging status. If it needs to be charged, the nearest charging station within the remaining battery level range is searched for, using A. The search algorithm plans charging routes. Users charge based on their vehicle's remaining battery power and the distance between their current location and destination, thus determining the charging station's electrical load at that moment. After charging is complete, the shortest route is planned based on current road resistance. If no further charging is needed, the vehicle follows the planned route. The system assesses the status of all electric vehicles; once all vehicle routes are planned, the system exits. The charging station load is calculated using the following formula: （8） In the formula: The load of the charging station located at node j at time t; The battery status of an electric vehicle when fully charged; Let A be the battery state of the i-th electric vehicle at time t; A is the traffic network matrix. , These refer to the charging characteristics and driving characteristics of electric vehicles, respectively. This represents the electric vehicle route optimization function, which yields charging demand and selected charging station information; (3) Obtain the remaining mileage information based on the current location information of the hydrogen fuel cell vehicle, and determine whether hydrogen refueling is needed based on the hydrogen refueling status; if needed, search for the nearest hydrogen refueling station within the remaining hydrogen status range for the vehicle, using A The search algorithm plans hydrogen refueling routes. Users refuel based on their vehicle's remaining hydrogen supply and the distance between their current location and destination, thus determining the hydrogen load at the refueling station. If no hydrogen is needed, the user continues along the planned route. The system assesses the arrival status of all hydrogen fuel cell vehicles within the system; once all vehicle routes are planned, the system exits the algorithm. The refueling station load is calculated using the following formula: （9） In the formula: The load of the hydrogen refueling station located at node j at time t; The state of hydrogen when a hydrogen fuel cell vehicle is fully refueled; Let A represent the hydrogen state of the i-th hydrogen fuel cell vehicle at time t; A is the traffic network matrix. , These are the hydrogen refueling characteristics and driving characteristics of hydrogen fuel cell vehicles, respectively. This represents the route optimization function for hydrogen fuel cell vehicles, which yields information on hydrogen refueling demand and selected hydrogen refueling stations.

9. The apparatus of claim 8, wherein, The traffic network consisting of N nodes and M branches is represented as an adjacency matrix A; The adjacency matrix A is represented as: （10） In equation (5): row vectors correspond to branches, and column vectors correspond to nodes; if the first... The branch road is from Node to Nodes =1、 =-1, and the rest of the components are 0; The framework system module includes a hydrogen production model, which is represented by equation (11): （11） In the formula: Let be the amount of hydrogen produced by the j-th electrolyzer during time period t; The electro-hydrogen conversion coefficient; The power consumed by the j-th electrolytic cell during time period t; Hydrogen refueling stations use hydrogen storage tanks to store excess hydrogen for backup. The hydrogen storage model is shown in the following formula: （12） In the formula: The hydrogen storage status of hydrogen refueling station at node j at time t; The hydrogen storage status of hydrogen refueling station at node j at time t-1; Let be the hydrogen production of the j-th electrolyzer during time period t at time t; Let the hydrogen load of the j-th hydrogen refueling station at time t be denoted as ; , These represent the amount of hydrogen at the beginning of the i-th hydrogen storage tank scheduling cycle and the amount of hydrogen at the end of the scheduling cycle, respectively. , These are the lower and upper limits for the hydrogen storage volume of hydrogen storage tanks, respectively. The power distribution system is formed by connecting with the regional transportation network. The power distribution network is a radial network, and its power flow equations are as follows: （13） （14） （15） （16） （17） （18） （19） （20） （21） （22） In the formula: and These represent the active power and reactive power injected into node j at time t, respectively. and These are the distribution network node set and the reference node, respectively. and These are the sets of child nodes and the set of parent nodes of node j, respectively. and These are respectively the active power and reactive power injected into the distribution network from the upper-level power grid; , , and These represent the active power output of the gas turbine, the load of the hydrogen refueling station, the wind power output, and the electrical load at node i at time t, respectively. and These represent the active power flow and reactive power on the line from node i to node j, respectively. and The voltage at node i and the reference voltage at time t are respectively; Let i be the voltage at time t; This represents the resistance between node i and node j; This represents the reactance between node i and node j; Indicates voltage deviation; and These represent the maximum active and reactive power transmitted by the line, respectively.

10. The apparatus of claim 8, wherein, The electric vehicle charging characteristics include charging station number, charging start time, charging start time, charging end time, battery state of charge, power consumption per kilometer, and battery state when fully charged; among which, the power consumption per kilometer is modeled based on different road conditions to determine the power consumption per unit mile of the electric vehicle. The driving characteristics of electric vehicles include vehicle number, vehicle location information, driving route, and vehicle departure and arrival node numbers. （23） In the formula: A set of charging characteristic parameters for electric vehicles; This is a set of parameters representing the driving characteristics of electric vehicles. It is the charging station number; This refers to the charging start time; This is the charging end time; for Real-time battery status; Electricity consumption per kilometer; The battery is fully charged. Number the vehicle; for Real-time vehicle location; For the driving route; The starting point of the vehicle; For vehicle destination; EV indicates electric vehicle; Electricity consumption model of electric vehicle per unit distance : （24） wherein: and are the unit energy consumption of electric vehicles for road classes I, II and III, respectively; denotes the average travel speed of a road section, which can be obtained from the road section length and the road travel time , the calculation formula is as follows: 。 11. The apparatus of claim 8, wherein, In the hydrogen fuel cell vehicle charging and driving module, the hydrogen refueling characteristics of the hydrogen fuel cell vehicle include the hydrogen refueling station number, refueling time, hydrogen state, hydrogen consumption per kilometer, and hydrogen state when fully refueled; the hydrogen consumption per kilometer is modeled based on different road conditions to determine the hydrogen consumption per unit mile of the hydrogen fuel cell vehicle. The driving characteristics of hydrogen fuel cell vehicles include vehicle number, vehicle location information, driving route, and vehicle departure and arrival node numbers. （25） In the formula: A set of hydrogen refueling characteristic parameters for hydrogen fuel cell vehicles; This is a set of driving characteristic parameters for hydrogen fuel cell vehicles. It is the hydrogen refueling station number; This refers to the hydrogenation time; for The state of hydrogen at any given moment; Hydrogen consumption per kilometer; This refers to the hydrogen state when the tank is full. Number the vehicle; for Real-time vehicle location; For the driving route; The starting point of the vehicle; For vehicle destination; HFCV indicates hydrogen fuel cell vehicle; Hydrogen fuel cell vehicle unit mileage hydrogen consumption model : In the formula: and These are the energy consumption per unit mileage for hydrogen fuel cell vehicles of road grade I, grade II, and grade III, respectively. The average speed of traffic on a road segment can be expressed by the length of the road segment. With road travel time The calculation formula is as follows: .

12. The apparatus of claim 8, wherein, The charging path planning module is used for taking the adjacency matrix A as a data basis, taking the A search algorithm as a basis, and integrating real-time road condition information to dynamically plan a travel path with the shortest time consumption for a user, including: 1) Obtain the real-time road resistance function and origin / end point nodes based on the traffic network adjacency matrix A, road resistance function, and OD pairs; divide all nodes in the road network into two sets S and W, respectively storing the nodes to be visited and the nodes that have been visited; 2) Initialization: Add the starting point to the set S, find the nodes that the starting point can reach, add them to the set S, and set the starting point as the parent node; 3) Remove the starting point from set S and add the starting point to set W; 4) Calculate the u-predicting function for the child nodes surrounding the current parent node. The predicting function is calculated using the following formula: In the formula: This represents the overall estimated value for that node; This represents the distance from the starting point to the current node; The Manhattan distance formula is used to represent the distance from the current node to the target node; 5) Select the node with the smallest estimated value, set it as the parent node, remove the node from S, and add it to the W set; 6) Repeat steps 4) and 5) until the target node is reached; 7) Output the nodes in set W to obtain the car's driving path.

13. The apparatus of claim 8, wherein, The hydrogen demand calculation module is used for: (1) Obtain traffic network information including road segment length, maximum traffic flow of road segment and road segment impedance, and read the travel demand of electric vehicles and hydrogen fuel cell vehicles in the traffic network, including travel time and initial location and vehicle destination, and randomly generate electric vehicle SOC and hydrogen fuel cell vehicle SOH; (2) Based on the current location information of the electric vehicle, the remaining mileage information is obtained. The electric vehicle determines whether it needs to be charged based on its charging status. If it needs to be charged, the nearest charging station within the remaining battery level range is searched for, using A. The search algorithm plans the charging route. The user charges the vehicle based on its remaining battery power and the distance between its current location and its destination, thus obtaining the current electrical load of the charging station. After charging is completed, the shortest driving route of the car is planned based on the current road resistance. If not needed, proceed according to the planned route; assess the status of all electric vehicles in the system, and exit when all vehicle routes are planned; the charging station load is calculated using the following formula: In the formula: The load of the charging station located at node j at time t; The battery status of an electric vehicle when fully charged; Let A be the battery state of the i-th electric vehicle at time t; A is the traffic network matrix. , These refer to the charging characteristics and driving characteristics of electric vehicles, respectively. This represents the electric vehicle route optimization function, which yields charging demand and selected charging station information; (3) Obtain the remaining mileage information based on the current location information of the hydrogen fuel cell vehicle, and determine whether hydrogen refueling is needed based on the hydrogen refueling status; if needed, search for the nearest hydrogen refueling station within the remaining hydrogen status range for the vehicle, using A The search algorithm plans hydrogen refueling routes. Users refuel based on their vehicle's remaining hydrogen supply and the distance between their current location and destination, thus determining the hydrogen load at the refueling station. If no hydrogen is needed, the user continues along the planned route. The system assesses the arrival status of all vehicles at their destinations and exits the system once all hydrogen fuel cell vehicle routes have been planned. The refueling station load is calculated using the following formula: In the formula: The load of the hydrogen refueling station located at node j at time t; The state of hydrogen when a hydrogen fuel cell vehicle is fully refueled; Let A represent the hydrogen state of the i-th hydrogen fuel cell vehicle at time t; A is the traffic network matrix. , These are the hydrogen refueling characteristics and driving characteristics of hydrogen fuel cell vehicles, respectively. This represents the route optimization function for hydrogen fuel cell vehicles, which yields information on hydrogen refueling demand and selected hydrogen refueling stations.

14. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the electric vehicle and hydrogen fuel cell vehicle charging and hydrogen refueling load simulation method as described in any one of claims 1 to 7.

15. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the electric vehicle and hydrogen fuel cell vehicle charging and hydrogen refueling load simulation method as described in any one of claims 1 to 7.

Images