Path planning scheme generation method, electronic equipment and storage medium

Abstract

The invention relates to the technical field of path planning, in particular to a path planning scheme generation method, electronic equipment and a storage medium. The path planning scheme generation method comprises the steps of obtaining starting point information and terminal point information of an electric vehicle and node information of each candidate node, constructing a node information network according to the starting point information, the terminal point information and the node information, constructing a target function according to a plurality of preset decision variables, traversing each node of the node information network according to the target function, and generating a path planning scheme. And obtaining target node information of each target node, and generating a path planning scheme of the electric vehicle according to the target node information. According to the embodiment of the invention, the method can provide the selection of the passing points for the user, guarantees that the user can smoothly reach the terminal point from the starting point under the condition that the electric quantity of the electric vehicle is insufficient, can guide the path planning according to the target function constructed by a plurality of preset decision variables, and facilitates the finding of a comprehensive optimal path for the user.

Description

Technical Field

[0001] The present invention relates to the technical field of path planning, and in particular to a path planning scheme generating method, electronic equipment and storage medium. Background Art

[0002] Traditional route planning solutions for electric vehicles from a starting point to a destination are primarily based on the shortest distance or the shortest time. This makes it difficult to find an optimal route, and users cannot freely choose waypoints. When the electric vehicle's battery is low, the user's route based on the shortest distance or the shortest time may not ensure smooth arrival from the starting point to the destination. Therefore, traditional route planning solutions are generally unable to provide users with an optimal and accessible route. Summary of the Invention

[0003] One purpose of this embodiment is to provide a path planning solution generation method, electronic device and storage medium to solve the technical problem of unreasonable path planning in traditional path planning solutions.

[0004] In a first aspect, an embodiment of the present invention provides a method for generating a path planning solution, comprising:

[0005] Obtaining the starting point information, the end point information, and the node information of each candidate node on the possible path from the starting point to the end point of the electric vehicle;

[0006] Constructing a node information network according to the starting point information, the end point information and the node information, wherein the node information network includes a starting point, an end point and candidate nodes;

[0007] Construct an objective function based on multiple preset decision variables;

[0008] Traversing each node of the node information network according to the target function to obtain target node information of each target node;

[0009] A path planning scheme for the electric vehicle is generated according to the target node information.

[0010] Optionally, traversing each node of the node information network according to the objective function to obtain target node information of each target node includes:

[0011] Starting from the starting point, querying node status information of each node in the node information network one by one;

[0012] Calculating a node cost value of each candidate node according to the node status information and the objective function;

[0013] A target node is selected from each candidate node in the node information network according to the node cost value, and target node information of the target node is obtained.

[0014] Optionally, each node includes at least one neighbor node, and selecting a target node from candidate nodes in the node information network according to the node cost value includes:

[0015] Adding the node cost value to a cost value queue;

[0016] Determine the target node cost value according to the cost value queue;

[0017] Determine the node to be traversed according to the target node cost value;

[0018] Determine whether the node to be traversed is an end point;

[0019] If it is the end point, the node to be traversed is used as the target node and the target node information of the target node is added to the node list, and a path planning scheme for the electric vehicle is generated according to the target node information of each target node in the node list;

[0020] If it is not the end point, the node to be traversed is taken as the target node and the target node information of the target node is added to the node list, the remaining power of the electric vehicle when it travels to the current target node is determined, the target neighbor node of the current target node is selected and the power consumption of the electric vehicle from the current target node to the target neighbor node is determined, and the target node is selected from each candidate node in the node information network according to the remaining power and the consumed power.

[0021] Optionally, the cost value queue includes node cost values arranged in sequence, each node cost value corresponds to a node, and before determining whether the node to be traversed is an end point, the process further includes:

[0022] Determine whether the node to be traversed exists in the node list;

[0023] If it exists in the node list, discard the target node cost value, re-determine the target node cost value according to the cost value queue and return to the step of determining the node to be traversed according to the target node cost value;

[0024] If it does not exist in the node list, the process proceeds to the step of determining whether the node to be traversed is the end point.

[0025] Optionally, before determining whether the node to be traversed is an end point, the method further includes:

[0026] generating a plurality of candidate driving paths based on the node information network;

[0027] Determine whether the node to be traversed and the target node in the node list are on a candidate driving path;

[0028] If it is on a candidate driving path, then enter the step of determining whether the node to be traversed is the end point;

[0029] If it is not on a candidate driving path, the target node cost value is discarded, the target node cost value is re-determined according to the cost value queue, and the process returns to the step of determining the node to be traversed according to the cost value queue.

[0030] Optionally, the node information of each node includes a travel distance and a travel time from the node to each neighboring node, the node information network includes rechargeable nodes, and selecting a target node from candidate nodes in the node information network according to the remaining power and the consumed power includes:

[0031] Determining whether the remaining power is greater than the consumed power;

[0032] If the power consumption is greater than the power consumption, the driving distance and driving time from the current target node to each neighboring node are obtained, the node status information of each neighboring node is determined based on the driving distance, the driving time, and the node status information of the current target node, each neighboring node is used as a node to be traversed, and the process returns to the step of calculating the node cost value of each node to be traversed based on the node status information and the objective function;

[0033] If it is not greater than the power consumption, determining whether the current target node is a rechargeable node;

[0034] If the node is rechargeable, determining a target charging capacity, adding the remaining capacity to the target charging capacity to obtain a target remaining capacity, and selecting a target node from each candidate node in the node information network according to the target remaining capacity;

[0035] If it is a non-rechargeable node, the target neighbor node is skipped, a target neighbor node of the current target node is reselected, and the process returns to the step of determining the power consumption of the electric vehicle when traveling from the current target node to the target neighbor node.

[0036] Optionally, the node information of the rechargeable node further includes the output power and charging price of at least one charger, and the selecting of the target node from the candidate nodes of the node information network according to the target remaining power includes:

[0037] Obtaining the battery capacity of an electric vehicle;

[0038] Determining whether the target remaining power is greater than the consumed power and less than the battery capacity;

[0039] If the target power consumption is greater than the power consumption and less than the battery capacity, the charging fee is calculated according to the target power consumption and the charging price, the charging time is calculated according to the target power consumption and the output power, and the node status information of the target neighbor node is determined according to the driving time, the driving distance, the charging fee, the charging time, and the node status information of the current target node, the target neighbor node is used as a candidate node, and the process returns to the step of calculating the node cost value of each candidate node according to the node status information and the objective function;

[0040] If it is not greater than the consumed power or not less than the battery capacity, the target charging power is re-determined and the process returns to the step of adding the target charging power to the remaining power.

[0041] Optionally, before determining whether the node to be traversed is an end point, the method further includes:

[0042] Determining whether the node status information of the node to be traversed includes charging information of the target node;

[0043] If the charging information of the target node is included, then the target node information of the target node is updated according to the charging information and the process proceeds to the step of determining whether the node to be traversed is the end point;

[0044] If the charging information of the target node is not included, the process directly proceeds to the step of determining whether the node to be traversed is the end point.

[0045] Optionally, when the candidate node is a rechargeable node, the node information of the rechargeable node includes reservation support status, the preset decision variables include cumulative consumption time, cumulative charging cost, cumulative driving distance and reservation support status, and the objective function is:

[0046] Cost=α*time_cost+β*money_cost+γ*distance-c*reservations

[0047] Among them, Cost is the cost value, α is the time weight, time_cost is the cumulative consumption time, β is the cost weight, money_cost is the cumulative charging cost, γ is the distance weight, distance is the cumulative driving distance, c is the preset coefficient, and reservations is the reservation support status. If reservation is supported, the value of reservations is the first preset value. If reservation is not supported, the value of reservations is the second preset value.

[0048] In a second aspect, an embodiment of the present invention provides an electronic device comprising a memory and a processor, wherein the memory is connected to the processor, and the processor is used to execute one or more computer programs stored in the memory. When the processor executes the one or more computer programs, the computer device implements the path planning solution generation method as described above.

[0049] In a third aspect, an embodiment of the present invention provides a storage medium storing a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the path planning solution generation method as described above.

[0050] Compared with the prior art, the embodiments of the present invention provide a path planning scheme generation method, electronic device and storage medium. The path planning scheme generation method includes: obtaining the starting point information, end point information and node information of each candidate node of the possible path from the starting point to the end point of the electric vehicle, constructing a node information network based on the starting point information, end point information and node information, the node information network includes the starting point, end point and candidate nodes, constructing an objective function based on multiple preset decision variables, traversing each node of the node information network according to the objective function, obtaining the target node information of each target node, and generating a path planning scheme for the electric vehicle based on the target node information. On the one hand, this embodiment can provide users with waypoint selection. When the electric vehicle is low on power, the user can try to choose a charging station as a waypoint, thereby ensuring that the user can smoothly reach the end point from the starting point. On the other hand, guiding the path planning according to the objective function constructed by multiple preset decision variables is conducive to finding a comprehensive optimal path for the user. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0052] Figure 1 A schematic diagram of a flow chart of a method for generating a path planning solution provided by an embodiment of the present invention;

[0053] Figure 2 A schematic diagram of a node information network provided by an embodiment of the present invention;

[0054] Figure 3 A schematic diagram of a target driving path provided by an embodiment of the present invention;

[0055] Figure 4A schematic diagram of the process of S14 in a path planning solution generation method provided by an embodiment of the present invention;

[0056] Figure 5 A schematic diagram of the process of S143 in a path planning solution generation method provided by an embodiment of the present invention;

[0057] Figure 6 A schematic structural diagram of a path planning solution generating method and apparatus provided by an embodiment of the present invention;

[0058] Figure 7 A schematic structural diagram of a traversal module in an output power scheduling device provided by an embodiment of the present invention;

[0059] Figure 8 A schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] To facilitate understanding of the present invention, the present invention will be described in more detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that when an element is described as "connected" to another element, it can be directly connected to the other element, or there can be one or more intervening elements therebetween. In addition, the terms "first," "second," etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance.

[0061] Unless otherwise defined, all technical and scientific terms used in this specification have the same meanings as those commonly understood by those skilled in the art to which this invention belongs. The terms used in this specification and in the description of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" as used in this specification includes any and all combinations of one or more of the relevant listed items.

[0062] The present invention provides a method for generating a path planning solution. Figure 1 , the path planning scheme generation method includes:

[0063] S11. Obtain the starting point information, the end point information, and the node information of each candidate node on the possible path from the starting point to the end point of the electric vehicle.

[0064] In this step, the starting point information includes the location information of the starting point, which is the starting point of the electric vehicle. If the starting point is a charging station, the starting point information also includes charging station information. This charging station information includes, but is not limited to, the charging station location, the output power of each charger in the charging station, charging prices, and reservation support. The preset support refers to whether the charging station supports advance charging reservations. Reservation support affects charging reliability to a certain extent. If advance charging reservations are supported and the user confirms that they need to charge at the charging station, the user can make a reservation in advance to ensure timely charging upon arrival. If advance charging reservations are not supported, if all chargers at the charging station are in use, the user may need to wait when they arrive to charge at the charging station, which may affect the charging reliability of the electric vehicle. The end point information includes the location information of the end point, which is the electric vehicle's destination. Candidate nodes are possible waypoints for the electric vehicle. That is, in the final planned target driving route, candidate nodes may or may not be located on the driving route. Multiple candidate nodes are distributed between the starting point and the end point. In some embodiments, the candidate nodes include chargeable nodes and / or non-chargeable nodes. A rechargeable node is a node that includes a charging station, while a non-rechargeable node is a node that does not include a charging station. It is understood that if the starting point is far from the end point and the electric vehicle has a low remaining charge, a charging station is more likely to be selected as a candidate node, so that the user can recharge the electric vehicle when passing through the candidate node to support the subsequent journey. Node information includes the location information of the candidate node. When the candidate node is a charging station, as previously mentioned, the node information includes charging station information. The charging station information can be referred to in the above description and will not be repeated here.

[0065] S12. Construct a node information network based on the starting point information, the ending point information, and the node information. The node information network includes the starting point, the ending point, and candidate nodes.

[0066] In this step, the node information network is a route network consisting of multiple nodes and the node information for each node. In the node information network, the starting point, end point, and candidate nodes are all nodes, and each node has at least two neighboring nodes. Neighboring nodes are nodes that can be directly connected to the current node. It is understood that in actual road conditions, a node and its neighboring nodes can form a road segment.

[0067] For example, see Figure 2 , the starting point A, candidate nodes S1, S2, S3, S4, S5, S6, S7 and S8, and the end point B constitute a node information network, such as Figure 2As shown, the neighbor nodes of starting point A include S1 and S2, the neighbor nodes of candidate node S1 include starting point A, candidate node S3 and S4, the neighbor nodes of candidate node S2 include starting point A, candidate node S4 and S5, the neighbor nodes of candidate node S3 include candidate nodes S1 and S6, the neighbor nodes of candidate node S4 include candidate nodes S1, S2 and S4, the neighbor nodes of candidate node S5 include candidate nodes S2 and S7, the neighbor nodes of candidate node S6 include candidate nodes S3, S4 and S8, the neighbor nodes of candidate node S7 include candidate node S5 and end point B, the neighbor nodes of candidate node S8 include candidate node S6 and end point B, and the neighbor nodes of end point B include candidate nodes S7 and S8.

[0068] like Figure 2 As shown, candidate nodes S1, S2, S3, S4, S5, S6, S7 and S8 are all rechargeable nodes. The charging station of candidate node S1 supports advance reservation charging, and the charging price is 1.5 yuan / kWh (kilowatt hour). The charging station includes two chargers, and the charger is a charging gun on a charging pile. The two chargers of the charging station can be two charging guns on the same charging pile, or two charging guns on different charging piles. The following description of the charger can refer to the explanation here. In the charging station, the output power of the two chargers is 60kW (kilowatt). The charging station of candidate node S2 does not support advance reservation charging, and the charging price is 1.3 yuan / kWh. The charging station includes two chargers, one of which has an output power of 60kW and the other has an output power of 90kW. The charging station of candidate node S3 supports advance reservation charging, and the charging price is 1.5 yuan / kWh. The charging station includes three chargers, one of which has an output power of 60kW and the output of the other two chargers is 60kW. The power is 120kW. The charging station of candidate node S4 supports advance reservation charging, and the charging price is 1.6 yuan / kWh. The charging station includes two chargers, and the output power of both chargers is 80kW. The charging station of candidate node S5 does not support advance reservation charging, and the charging price is 1.4 yuan / kWh. The charging station includes one charger with an output power of 100kW. The charging station of candidate node S6 supports advance reservation charging, and the charging price is 1.1 yuan / kWh. The charging station includes two chargers, one of which has an output power of 60kW and the other has an output power of 70kW. The charging station of candidate node S7 supports advance reservation charging, and the charging price is 1.7 yuan / kWh. The charging station includes two chargers, one of which has an output power of 60kW and the other has an output power of 90kW. The charging station of candidate node S8 does not support advance reservation charging, and the charging price is 1 yuan / kWh. The charging station includes two chargers, and the output power of both chargers is 110kW.

[0069] Because each node's node information (including starting and ending information) includes at least location information, electronic devices can directly calculate or obtain the driving distance or driving time between two adjacent nodes based on each node's location information through mapping software. The driving distance is the distance an electric vehicle travels from the current node to a neighboring node, and the driving time is the time it takes for an electric vehicle to travel from the current node to a neighboring node. The driving distance and driving time can be used to subsequently traverse the node information network.

[0070] For example, if Figure 2 As shown, the electronic device can obtain the driving distance of the road section as 100 km (kilometers) and the driving time as 1.5 hours (hours) based on the position information of the two adjacent nodes A and S1, the driving distance of the road section as 180 km and the driving time as 2.5 hours based on the position information of the two adjacent nodes S1 and S3, the driving distance of the road section as 120 km and the driving time as 1.8 hours based on the position information of the two adjacent nodes S1 and S4, the driving distance of the road section as 200 km and the driving time as 2.8 hours based on the position information of the two adjacent nodes S2 and S4, the driving distance of the road section as 100 km and the driving time as 1.4 hours based on the position information of the two adjacent nodes S2 and S5, and the driving distance of the road section as 250 km , the driving time is 3.5 hours. According to the position information of the two adjacent nodes S3 and S6, the driving distance of this section is 200km, and the driving time is 3h. According to the position information of the two adjacent nodes S4 and S6, the driving distance of this section is 80km, and the driving time is 1h. According to the position information of the two adjacent nodes S5 and S7, the driving distance of this section is 150km, and the driving time is 2 hours. According to the position information of the two adjacent nodes S6 and S8, the driving distance of this section is 120km, and the driving time is 1.8 hours. According to the position information of the two adjacent nodes S7 and B, the driving distance of this section is 180km, and the driving time is 2.6h. According to the position information of the two adjacent nodes S8 and B, the driving distance of this section is 100km, and the driving time is 1.5h.

[0071] It is understandable that different driving distances do not mean different driving times, and the same driving distances do not mean the same driving times. This is because there may be various uncertainties such as vehicle congestion in real-time road conditions. When the driving distance is the same, the driving time required on a congested route may be longer than the driving time required on a smooth route.

[0072] For example, if Figure 2As shown, although the driving distance between two adjacent nodes S1 and S4 and the driving distance between two adjacent nodes S3 and S6 are both 200 km, the driving time between two adjacent nodes S1 and S4 is shorter than the driving time between two adjacent nodes S3 and S6.

[0073] In some embodiments, the electronic device may generate multiple candidate driving routes based on a node information network.

[0074] The candidate driving path is a potential target driving path, and the target driving path is the driving path ultimately planned by the electronic device for the user. It is understandable that each candidate path may become the target driving path, and each candidate path includes multiple nodes.

[0075] For example, electronic devices can be Figure 2 The node information network shown generates five candidate driving paths. The first candidate driving path passes through nodes A, S1, S3, S6, S8 and B in sequence. The second candidate driving path passes through nodes A, S1, S4, S6, S8 and B in sequence. The third candidate driving path passes through nodes A, S2, S4, S6, S8 and B in sequence. The fourth candidate driving path passes through nodes A, S2, S4, S1, S3, S6, S8 and B in sequence. The fifth candidate driving path passes through nodes A, S2, S5, S7 and B in sequence.

[0076] S13. Construct an objective function based on multiple preset decision variables.

[0077] In this step, the preset decision variables are decision variables set by the user according to actual needs or expected optimization goals. Decision variables are unknown quantities that need to be solved in the optimization problem, and their values directly affect the results of the objective function and the degree of satisfaction of the constraints. The objective function plays a core guiding role in the search for the best driving path. It determines the ultimate goal to be optimized by the search algorithm and is a standard for measuring the "goodness" of a path. In some embodiments, the preset decision variables include but are not limited to the cumulative consumption time, cumulative charging costs, cumulative driving distance or reservation support status. The cumulative driving time is the cumulative time consumed by the electric vehicle from the starting point to the current node. It can be understood that when the electric vehicle is charged at one or several nodes during the process of driving from the starting point to the current node, the cumulative consumption time also includes the charging time. In multi-objective optimization problems (such as time, cost, distance, etc.), time cost is a key factor, especially when it is necessary to optimize the stay time at the charging station. The cumulative charging fee is the cumulative charging fee paid by the electric vehicle from the starting point to the current node. By tracking the charging fees that have been paid, it is helpful for the electronic device to plan a more economical driving route. The cumulative driving distance is the cumulative distance traveled by the electric vehicle from the starting point to the current node. Distance is one of the common optimization goals in route planning, especially when battery life needs to be considered. The reservation support status is whether advance reservation charging is supported. In some embodiments, the objective function is:

[0078] Cost=α*time_cost+β*money_cost+γ*distance-c*reservations

[0079] Among them, Cost is the cost value, α is the time weight, time_cost is the cumulative consumption time, β is the cost weight, money_cost is the cumulative charging cost, γ is the distance weight, distance is the cumulative driving distance, c is the preset coefficient, and reservations is the reservation support status. If reservation is supported, the value of reservations is the first preset value. If reservation is not supported, the value of reservations is the second preset value.

[0080] In this embodiment, the objective function incorporates the decision variable of reservation support status. For two sets of data, if the cumulative consumption time, cumulative charging cost, and cumulative driving time of the two sets of data are identical, and only the reservation support status differs, then the electronic device can preferentially select the route corresponding to the set of data that supports advance charging reservations. This avoids the problem of the electronic device being unable to make a choice when the cumulative consumption time, cumulative charging cost, and cumulative driving time are identical, thereby ensuring charging reliability. It is understood that since reservation support status is introduced into the objective function to avoid the extremely rare situation where the cumulative consumption time, cumulative charging cost, and cumulative driving time of multiple sets of data are highly consistent, it cannot serve as a key influencing factor in route planning. Therefore, to prevent the reservation support status from excessively influencing route planning, in some embodiments, the preset coefficient c can be set to a value smaller than the duration weight α, cost weight β, and distance weight γ, thereby weakening the influence of reservation support status. It can also be understood that since the appointment support situation does not play a critical role in route planning, in some embodiments, the appointment support situation may not be used as a decision variable of the above-mentioned objective function, that is, the preset decision variables of the objective function only include the cumulative consumption time, the cumulative charging cost and the cumulative driving time.

[0081] In some implementations, the first preset value is 1 and the second preset value is 0.

[0082] Since this embodiment constructs an objective function through multiple preset decision variables such as cumulative consumption time, cumulative charging costs, and cumulative driving distance, and uses the objective function to guide path planning, this embodiment can find a comprehensive optimal path for the user based on multiple dimensions, ensuring the rationality of path planning.

[0083] In some embodiments, the values of the duration weight α, the cost weight β, the distance weight γ, and the preset coefficient c are greater than 0, and the goal of the objective function is to minimize the cost.

[0084] In this embodiment, as described above, minimizing the cost value of the objective function means that in the process of the electric vehicle traveling from the starting point to the end point, the cumulative consumption time is required to be as short as possible, the cumulative charging cost is as low as possible, and the cumulative driving distance is as short as possible. Therefore, by finding the path with the smallest cost value, the overall optimal path can be found, and the electronic device can use this path as the target driving path of the electric vehicle.

[0085] Because different decision variables have different dimensions or scales, in order to avoid biasing towards large-scale features when calculating the cost value of the objective function, in some embodiments, the electronic device can normalize the accumulated consumption time, accumulated charging costs, and accumulated driving distance to obtain normalized values for time, cost, and distance. When calculating the cost value, these normalized values for time, cost, and distance are substituted into the objective function. Therefore, this approach can map data of different dimensions or ranges to a unified scale, solving problems caused by data scale differences and improving the accuracy of cost value calculation.

[0086] S14. Traverse each node of the node information network according to the target function to obtain target node information of each target node.

[0087] In this step, when traversing the node information network according to the objective function, the electronic device can use the starting point as the first node of the traversal and the end point as the end point of the traversal. That is, the electronic device needs to start traversing from the starting point, and when it traverses to the end point, the traversal process ends. The electronic device can traverse each candidate node in the node information network according to the goal of minimizing the cost value of the objective function, thereby finding the target node that the electric vehicle needs to pass through, that is, the target node is the node used to generate the target driving path. The target node information at least includes the node information of the target node, such as the location information of the target node. When the electric vehicle generates charging behavior at the target node, the target node information can also include the charging information of the target node, such as the amount of electricity charged by the electric vehicle at the target node.

[0088] In some embodiments, the node information network includes road network information and charging station information. The road network information includes the relationship information between each node and each neighbor node. The relationship information includes the location information of a node's neighbor nodes and the driving distance and driving time from the node to each neighbor node. The charging station information includes the output power, charging price and reservation support status of each charger of each rechargeable node.

[0089] For example, corresponding to Figure 2 The node information network shown, the road network information is as follows:

[0090] Graph = {node location information: [(neighbor node location information, driving distance, driving time)]}

[0091] Graph = {

[0092] "A":[("S1",100.0,1.5),("S2",180.0,2.5)],

[0093] "S1":[("A",100.0,1.5),("S3",120.0,1.8),("S4",200.0,2.8)],

[0094] "S2":[("A",180.0,2.5),("S4",100.0,1.4),("S5",250.0,3.5)],

[0095] "S3":[("S1",120.0,1.8),("S6",200.0,3.0)],

[0096] "S4":[("S1",200.0,2.8),("S2",100.0,1.4),("S6",80.0,1.0)],

[0097] "S5":[("S2",250.0,3.5),("S7",150.0,2.0)],

[0098] "S6":[("S3",200.0,3.0),("S4",80.0,1.0),("S8",120.0,1.8)],

[0099] "S7":[("S5",150.0,2.0),("B",180.0,2.6)],

[0100] "S8":[("S6",120.0,1.8),("B",100.0,1.5)],

[0101] "B":[("S7",180.0,2.6),("S8",100.0,1.5)],

[0102] In the above road network information, the unit of driving distance is kilometers and the unit of driving time is hours.

[0103] Corresponding to Figure 2 The node information network shown, the charging station information is as follows:

[0104] Stations = {Charging station location information: [(output power, charging price, reservation support)]}

[0105] class Charger(NamedTuple):

[0106] power:float

[0107] price:float

[0108] reservable:bool

[0109] Stations = {

[0110] "S1":[Charger(60,1.5,True),Charger(60,1.5,True)],

[0111] "S2":[Charger(90,1.3,False),Charger(60,1.3,False)],

[0112] "S3":[Charger(120,1.2,True),Charger(120,1.2,True),Charger(60,1.2,False)],

[0113] "S4":[Charger(80,1.6,True),Charger(80,1.6,True)],

[0114] "S5":[Charger(100,1.4,False)],

[0115] "S6":[Charger(70,1.1,True),Charger(60,1.1,True)],

[0116] "S7":[Charger(60,1.7,True),Charger(90,1.7,False)],

[0117] "S8":[Charger(110,1.0,False),Charger(110,1.0,False)],

[0118] }

[0119] In the above charging station information, the unit of output power is kilowatt, the charging price is the price per kilowatt-hour, and the unit is yuan. The reservation support status includes True and False. True means that advance reservation charging is supported, and False means that advance reservation charging is not supported.

[0120] S15. Generate a path planning solution for the electric vehicle based on the target node information.

[0121] In this step, the path planning scheme may include the target driving path and charging planning information. The target driving path is a path consisting of a starting point, a target node, and an end point. For example, see Figure 3 , the target nodes include nodes S2, S4, S6 and S8, and the electronic device can plan the following according to the starting point A, the target nodes S2, S4, S6, S8 and the end point B: Figure 3As shown in the target driving path, in this target driving path, the electric vehicle starts from the starting point A, passes through nodes S2, S4, S6 and S8 in sequence, and finally arrives at the end point B. The charging planning information includes the charging information at each node where the charging behavior occurs. For example, when the electric vehicle passes through node S2, it charges 30kWh (kilowatt-hours) of electricity at node S2, when it passes through node S6, it charges 20kWh of electricity at node S6, and when it passes through node S8, it charges 20kWh of electricity at node S8. It can be understood that when the electric vehicle is traveling according to the target driving path, when it passes through a node that includes charging information, the user needs to charge the electric vehicle at that node according to the charging information.

[0122] In general, on the one hand, this embodiment can provide users with waypoint selection. When the electric vehicle is low on power, the user can try to choose a charging station as a waypoint, thereby ensuring that the user can smoothly reach the destination from the starting point. On the other hand, the path planning is guided by the objective function constructed by multiple preset decision variables, which is conducive to finding a comprehensive optimal path for the user.

[0123] In some embodiments, see Figure 4 , S14 includes:

[0124] S141, starting from the starting point, querying the node status information of each node in the node information network one by one;

[0125] In this step, the node status information is used to represent the current state of each node. The node status information includes, but is not limited to, the location information of each node, the remaining power of the electric vehicle when traveling to each node, the accumulated consumption time, the accumulated charging cost, the accumulated travel distance, etc.

[0126] It is understandable that since the electric vehicle has not yet generated the cumulative consumption time, cumulative charging cost, and cumulative driving distance when it starts at the starting point, the electronic device needs to initialize the state of the starting point before obtaining the node status information of each node to be traversed, wherein the cumulative consumption time, cumulative charging cost, and cumulative driving distance are all set to 0. After the state initialization is completed, the electronic device can obtain the node status information of the starting point and traverse each node of the node information network from the starting point.

[0127] S142. Calculate the node cost of the candidate node based on the node status information and the objective function.

[0128] In this step, the electronic device may substitute the node status information of the candidate node into the objective function to obtain the node cost value of the candidate node.

[0129] For example, the candidate node is S1, and the node status information of node S1 is (E_S1, tc_S1, mc_S1, d_S1, R_S1), where E_S1 represents the remaining power of the electric vehicle when it travels to node S1, E_S1 = E_A-E_AS1, E_A represents the remaining power of the electric vehicle at starting point A, E_AS1 represents the power consumption of the electric vehicle when it travels from starting point A to node S1, mc_S1 represents the cumulative charging cost generated by the electric vehicle in the process of traveling from starting point A to node S1, and d_S1 represents the total charging cost of the electric vehicle when it travels from starting point A to node S1. shows the cumulative distance traveled by the electric vehicle from starting point A to node S1, R_S1 represents the reservation support status. When node S1 is a rechargeable node and supports advance reservation charging, R_S1 can be set to 1. When node S1 is a rechargeable node and supports advance reservation charging, or node S1 is a non-rechargeable node, R_S1 can be set to 0. Substituting the node status information into the objective function, the node cost value of node S1 can be obtained as Cost_S1 = α*tc_S1+β*mc_S1+γ*d_S1-c*R_S1.

[0130] S143. Traverse the node information network according to the node cost value to obtain the target node information of each target node.

[0131] In this step, each time the electronic device traverses a target node, it will determine the node status information of each neighbor node of the target node, and then substitute the node status information of each neighbor node into the objective function to obtain the node cost value of each neighbor node. According to the node cost value of each neighbor node, the target node is determined from the neighbor nodes, and the neighbor nodes are traversed based on the target node. This cycle is repeated until the end point is reached, thus realizing the traversal of the node information network. After the traversal is completed, the electronic device can obtain multiple target nodes and the target node information of each target node.

[0132] When determining a target node from neighboring nodes based on the node cost of each neighboring node, the electronic device can determine the neighboring node with the smallest node cost as the target node. Because the electronic device selects the neighboring node with the smallest node cost as the target node each time it traverses a neighboring node of a target node, traversing the node information network in this manner ensures that the total cost of the target driving path ultimately generated is the smallest among all candidate driving paths, i.e., the target driving path is the overall optimal path.

[0133] In some embodiments, see Figure 5 , S143 includes:

[0134] S1431. Add the node cost value to the cost value queue.

[0135] S1432. Determine the target node cost value according to the cost value queue.

[0136] S1433. Determine the node to be traversed according to the cost value of the target node.

[0137] S1434. Determine whether the node to be traversed is the end point.

[0138] S1435. If it is not the end point, the node to be traversed will be taken as the target node and the target node information of the target node will be added to the node list to determine the remaining power of the electric vehicle when it travels to the current target node, select the target neighbor node of the current target node and determine the power consumption of the electric vehicle when traveling from the current target node to the target neighbor node, and select the target node from each candidate node in the node information network based on the remaining power and the power consumption.

[0139] S1436. If it is the end point, the node to be traversed is used as the target node and the target node information of the target node is added to the node list, and a path planning scheme for the electric vehicle is generated according to the target node information of each target node in the node list.

[0140] In S1431, the cost value queue is used to sequentially store the node cost value of each node to be traversed. As previously described, assuming that the electronic device traverses to neighboring nodes S1 and S2 of starting point A, that is, nodes S1 and S2 are both candidate nodes, as previously described, the node state information of node S1 can be expressed as (E_S1, tc_S1, mc_S1, d_S1, R_S1), and similarly, the node state information of node S2 can be expressed as (E_S2, tc_S2, mc_S2, d_S2, R_S2). The definition of each parameter in the node state information can refer to the above embodiment and will not be repeated here.

[0141] Please combine Figure 2 , the driving time from starting point A to node S1 is 1.5 hours, then tc_S1=1.5, the electric vehicle has not yet generated charging behavior when driving from starting point A to node S1, then mc_S1=0, the driving distance from starting point A to node S1 is 100 kilometers, then d_S1=100, node S1 is a rechargeable node and supports advance charging reservation, then R_S1=1, the electronic device substitutes tc_S1=1.5, mc_S1=0, d_S1=100 and R_S1=1 into the objective function, and obtains the node cost value Cost_S1=1.5α+100γ-c of node S1.

[0142] Similarly, the node status information of node S2 can be expressed as (E_S2, tc_S2, mc_S2, d_S2, R_S2). Figure 2, E_S2=E_A-E_AS2, tc_S2=2.5, mc_S2=0, d_S2=180, R_S2=0, the electronic device substitutes tc_S2=2.5, mc_S1=0, d_S1=180 and R_S1=0 into the objective function to obtain the node cost value Cost_S2=2.5α+180γ of node S2.

[0143] In some embodiments, the cost value queue includes node cost values arranged in sequence, each node cost value corresponding to a node. In the cost value queue, the node cost values can be arranged in ascending order or in descending order. Assuming that Cost_S1>Cost_S2, the electronic device can add the node cost value Cost_S1 of node S1 and the node status information Cost_S2 of node S2 to the cost value queue shown in Table 1 below (the node cost values are arranged in ascending order):

[0144] Table 1

[0145] Serial number Node cost value node 1 Cost_S2 S2 2 Cost_S1 S1

[0146] As shown in Table 1 above, the cost value queue includes node cost values Cost_S1 and Cost_S2. There is a corresponding relationship between each node cost value and the node. As shown in Table 1 above, the node corresponding to the node cost value Cost_S1 is S1, and the node corresponding to the node cost value Cost_S2 is S2.

[0147] In S1432, the electronic device may select the smallest node cost value from the cost value queue as the target node cost value. For example, as shown in Table 1 above, the electronic device may select the smallest node cost value Cost_S2 from the cost value queue as the target node cost value.

[0148] In S1433, the electronic device can determine that the node corresponding to the target node cost value is the node to be traversed based on the correspondence between the node cost value and the node. As mentioned above, the target node cost value is Cost_S2, and the electronic device can determine that the node S2 corresponding to Cost_S2 is the node to be traversed.

[0149] In S1434, if the node to be traversed is the end point, the electronic device can end the traversal; if the node to be traversed is not the end point, the electronic device needs to traverse. Figure 2 As shown, when the node to be traversed is S8, since node S8 is not the end point, the electronic device needs to continue traversing the neighboring nodes of node S8. When the node to be traversed is B, since node B is the end point, the electronic device can end the traversal and generate a path planning plan for the electric vehicle based on the target node information of each target node in the node list.

[0150] In some embodiments, the electronic device can determine whether the node to be traversed exists in the node list. If it exists in the node list, the target node cost value is discarded, the target node cost value is re-determined based on the cost value queue and the step of determining the node to be traversed based on the target node cost value is returned. If it does not exist in the node list, the step of determining whether the node to be traversed is the end point is entered.

[0151] In this embodiment, if the node to be traversed exists in the node list, it indicates that the node to be traversed is a target node that has already been traversed and does not need to be traversed again. Therefore, the electronic device can skip the node to be traversed when traversing neighbor nodes. For example, if the node to be traversed is S2, as described above, the neighbor nodes of the node to be traversed S2 are starting point A, nodes S4, and S5. At this time, even if starting point A has the lowest node cost, it cannot be selected because starting point A has already been traversed. If starting point A, which has already been traversed, is used as the target node, the traversal may fail or the optimal neighbor node cannot be selected. Therefore, this embodiment can ensure traversal reliability while ensuring that the optimal neighbor node can be selected.

[0152] In some embodiments, the electronic device generates multiple candidate driving paths based on the node information network, and determines whether the node to be traversed and the target node in the node list are on a candidate driving path. If they are on a candidate driving path, the step of determining whether the node to be traversed is the end point is entered. If it is not on a candidate driving path, the target node cost value is discarded, the target node cost value is re-determined according to the cost value queue, and the step of determining the node to be traversed according to the cost value queue is returned to.

[0153] In this embodiment, since the target driving path is one of multiple candidate driving paths, the target node on the target driving path must be on one or several candidate driving paths. If the node to be traversed and the target node in the node list are on a candidate driving path, it means that the node to be traversed can be used to generate the target driving path. If the node to be traversed and the target node in the node list are not on a candidate driving path, it means that the node to be traversed cannot be used to generate the target driving path. Therefore, the electronic device can skip the node to be traversed when traversing neighbor nodes.

[0154] For example, if the node to be traversed is S6, as mentioned above, the neighboring nodes of the node to be traversed S6 are nodes S3 and S8 respectively. Assume that the target nodes in the node list include starting point A, nodes S2 and S4. Since starting point A, nodes S2, S4, S6 and S3 are not on any candidate driving path, node S3 cannot be used to generate the target driving path. At this time, the electronic device can skip node S3. Since starting point A, nodes S2, S4, S6 and S8 are on the candidate driving path composed of starting point A, nodes S2, S4, S6, S8 and end point B, node S8 can be used to generate the target driving path.

[0155] Therefore, this embodiment can avoid planning a target driving path that is not within the expected consideration range, ensuring that the target driving path is reachable, thereby ensuring the rationality of the path planning.

[0156] In S1435, for example, as mentioned above, the node to be traversed is the starting point A. Since the starting point A is not the end point, the electronic device can use the starting point A as the target node and add the target node information of the starting point A to the node list. Then the electronic device needs to traverse the neighboring nodes S1 and S2 of the starting point A to extend the optimal path. When traversing the neighboring nodes S1 and S2 of the starting point A, if the remaining power of the electric vehicle at the starting point A is sufficient, the remaining power of the electric vehicle can support it to travel to the neighboring node S1 or S2. If the remaining power of the electric vehicle at the starting point A is not sufficient, the remaining power of the electric vehicle may not be able to support it to travel to the neighboring node S1 or S2. Moreover, since the driving distance of the electric vehicle from the starting point A to the neighboring nodes S1 and S2 is different, the power consumed during the period is also different. Therefore, in order to ensure that the electric vehicle can travel smoothly from the starting point A to its neighboring node, the electronic device can give priority to selecting the neighboring node whose remaining power of the electric vehicle is sufficient to support its arrival as the target node.

[0157] In S1436, for example, as mentioned above, the node to be traversed is the end point B. Since the end point B is the end point, the electronic device can use the end point B as the target node and add the target node information of the end point B to the node list. At this time, the node list includes the starting point A, the end point B and at least one candidate node. Assuming that the at least one candidate node includes nodes S2, S4, S6 and S8, the electronic device can generate a path planning plan for the electric vehicle based on the starting point A, the end point B and the target node information of nodes S2, S4, S6 and S8.

[0158] In some embodiments, when traversing neighbor nodes based on the remaining power and the consumed power, the electronic device can determine whether the remaining power is greater than the consumed power. If it is greater than the consumed power, the driving distance and driving time from the current target node to each neighbor node are obtained, and the node status information of each neighbor node is determined based on the driving distance, driving time and the node status information of the current target node. Each neighbor node is used as a candidate node and the step of calculating the node cost of each candidate node based on the node status information and the objective function is returned. If it is not greater than the consumed power, it is determined whether the current target node is a rechargeable node. If it is a rechargeable node, the target charging power is determined, the remaining power is added to the target charging power to obtain the target remaining power, and the neighbor nodes are traversed according to the target remaining power. If it is a non-rechargeable node, the target neighbor node is skipped, the target neighbor node of the current target node is reselected and the step of calculating the power consumption of the electric vehicle from the current target node to the target neighbor node is returned.

[0159] In this embodiment, if the remaining power is greater than the consumed power, the electronic device only needs to select the optimal neighbor node as the target node based on the node cost value of each neighbor node. As mentioned above, the current target node is starting point A, the remaining power of the electric vehicle at starting point A is E_S1, the power consumption from starting point A to neighbor node S1 is E_AS1, and the power consumption from starting point A to neighbor node S2 is E_AS2. If E_S1 is greater than E_AS1 and E_AS2, it means that the electric vehicle can travel from starting point A to neighbor nodes S1 and S2. Assuming that the node cost value of neighbor node S1 is greater than the node cost value of neighbor node S2, the electronic device can use the optimal neighbor node S2 as the target node.

[0160] If the remaining power is less than the consumed power, it means that the electric vehicle cannot travel to the neighboring nodes if the power is not replenished in time at the current target node. As mentioned above, the current target node is S2, and the neighboring nodes of the current target node S2 include starting points A, S4 and S5. Since starting point A is a target node that has been traversed, starting point A will not be considered. At this time, the electronic device will select the best neighboring node from neighboring nodes S4 and S5 as the target node. The remaining power of the electric vehicle at node S2 is E_S2, the power consumption from node S2 to neighboring node S4 is E_S2S4, and the power consumption from node S2 to neighboring node S5 is E_S2S5. If E_S2 is not greater than E_S2S4 and E_S2S5, it means that the electric vehicle cannot travel to neighboring nodes S4 and S5 without replenishing power at node S2. It can be understood that in this case, if node S2 is a non-rechargeable node, the electric vehicle may not be able to travel from node S2 to neighboring nodes S4 and S5, which may result in the traversal being terminated without finding the target driving path. If node S2 is a non-rechargeable node, and assuming that E_S2 is greater than E_S2S4 and less than E_S2S5, it can be understood that at this time, even if the node cost value of the current neighbor node S5 is less than the node cost value of the neighbor node S4, the electronic device will not consider selecting the neighbor node S5 as the target node, because the remaining power of the electric vehicle at node S2 cannot support its travel to the neighbor node S5. Therefore, at this time, the electronic device will skip the current neighbor node S5, reselect the neighbor node S4 of the current target node S2 as the current neighbor node and return to the step of calculating the power consumption of the electric vehicle when traveling from the current target node to the current neighbor node.

[0161] As mentioned above, if the remaining power E_S2 of the current target node S2 is not greater than E_S4S1 and E_S4S6, and the current target node S2 is a rechargeable node, the electronic device can add the target charging power to E_S2 and then traverse the neighboring nodes S4 and S5 according to the target remaining power after charging.

[0162] It is understandable that if charging is required at the current target node, the electronic device can attempt to charge according to the target charging power. The target charging power may include 10kwh, 20kwh, 30kwh, 50kwh and 70kwh, etc. As mentioned above, the current target node is S2, and the remaining power of the electric vehicle at node S2 is E_S2. The electric vehicle cannot travel to neighboring nodes S4 and S5 without replenishing power at node S2. Then, the electronic device can add 10kwh to the remaining power E_S2 of node S2, or add 20kwh, 30kwh, 50kwh or 70kwh, etc. to obtain the target remaining power of node S2.

[0163] In some embodiments, when a target node is selected from various candidate nodes in a node information network based on a target remaining power, the electronic device can obtain the battery capacity of the electric vehicle and determine whether the target remaining power is greater than the consumed power and less than the battery capacity. If it is greater than the consumed power and less than the battery capacity, the charging fee is calculated based on the target charging power and the charging price, the charging time is calculated based on the target charging power and the output power, and the node status information of the target neighbor node is determined based on the driving time, driving distance, charging fee, charging time and the node status information of the current target node. The target neighbor node is used as a candidate node and the step of calculating the node cost value of each candidate node based on the node status information and the objective function is returned. If it is not greater than the consumed power or not less than the battery capacity, the target charging power is re-determined and the step of adding the remaining power to the target charging power is returned.

[0164] For example, as mentioned above, the current target node is S2. Assuming that the remaining power E_S2 of the electric vehicle at node S2 is 1kWh, the power consumption E_S2S4 of the electric vehicle when traveling from the current target node S2 to the neighboring node S4 is 22kWh, and the power consumption E_S2S5 of the electric vehicle when traveling from the current target node S2 to the neighboring node S5 is 35kWh. Since 1kWh is less than 22kWh and 35kWh, the electronic device tries to add 10kWh, 20kWh, 30kWh, 50kWh and 70kWh to 1kWh in turn, and obtains the target remaining power of 11kWh, 21kWh, 31kWh, 51kWh and 71kWh respectively. Assuming that the battery capacity E_max of the electric vehicle is 70kWh, for the neighboring node For node S4, since 31kWh and 51kWh are greater than the power consumption of 22kWh and less than 70kWh, the charging attempts of 30kWh and 50kWh will be adopted, while 11kWh and 21kWh are both less than 22kWh, and 71kWh is greater than 70kWh, so the charging attempts of 10kWh, 20kWh and 70kWh will not be adopted. For the neighboring node S5, since 51kWh is greater than the power consumption of 35kWh and less than 70kWh, the charging attempt of 50kWh will be adopted, while 11kWh, 21kWh and 31kWh are all less than 35kWh, and 71kWh is greater than 70kWh, so the charging attempts of 10kWh, 20kWh, 30kWh and 70kWh will not be adopted.

[0165] Assume that the node status information of the current neighbor node S4 is (E_S2, tc_S2, mc_S2, d_S2, R_S2). As mentioned above, for the neighbor node S4, since the adopted target charging power includes 30kWh and 50kWh, the neighbor node S2 has two chargers with output power of 60kW and 90kW to choose from. Therefore, when the electronic device updates the node status information of the neighbor node S4, it needs to update four sets of node status information. When updating each set of node status information, the electronic device can multiply the target charging power by the charging price to obtain the charging fee, and use the target charging power at the output power to obtain the charging time.

[0166] For the combination of 30kWh and 60kW, the charging cost mc_30 = 30*1.3 = 39 yuan, the charging time tcc_30 = 30 / 60 = 0.5h, and the node status information of the neighbor node S4 is (E_S4, tc_S4, mc_S4, d_S4, R_S4), where E_S4 = E_S2+30, tc_S4 = tc_S2+1.4+0.5, mc_S4 = mc_S2+39, d_S4 = d_S2+100, and R_S2 = 1.

[0167] For the combination of 50kWh and 60kW, the charging cost mc_50 = 50*1.3 = 65 yuan, the charging time tcc_50 = 50 / 60 = 0.833h, and the node status information of the neighbor node S4 is (E_S4, tc_S4, mc_S4, d_S4, R_S4), among which E_S4 = E_S2+50, tc_S4 = tc_S2+1.4+0.833, mc_S4 = mc_S2+65, d_S4 = d_S2+100, and R_S2 = 1.

[0168] For the node status information update of the combination of 30kWh and 90kW and the combination of 50kWh and 90kW, reference may be made to the above embodiments, which will not be repeated here.

[0169] For neighbor node S5, since the adopted target charging capacity is 50 kWh and neighbor node S2 has two charger options, one with a 60 kW output and the other with a 90 kW output, the electronic device needs to update two sets of node status information when updating the node status information of neighbor node S5. The update of each set of node status information for neighbor node S5 can be referred to in the above embodiment and will not be repeated here.

[0170] The electronic device can substitute the four sets of node status information of the neighboring node S4 into the objective function, and obtain the node cost values Cost_S4_30 / 60, Cost_S4_50 / 60, Cost_S4_30 / 90 and Cost_S4_50 / 90 respectively, where Cost_S4_30 / 60 is the node cost value corresponding to the combination of 30kWh and 60kW, Cost_S4_50 / 60 is the node cost value corresponding to the combination of 50kWh and 60kW, and Cost_S4_30 / 90 is 30kWh. The node cost corresponding to the combination of 50kWh and 90kW is Cost_S4_50 / 90, and the node cost corresponding to the combination of 50kWh and 90kW is Cost_S4_50 / 90. Substituting the two sets of node state information of neighboring node S5 into the objective function, the node cost values Cost_S5_50 / 60 and Cost_S5_50 / 90 are obtained respectively. Among them, Cost_S5_50 / 60 is the node cost corresponding to the combination of 50kWh and 60kW, and Cost_S5_50 / 90 is the node cost corresponding to the combination of 50kWh and 90kW. Therefore, in some embodiments, the electronic device can obtain the cost value sequence shown in Table 2 below:

[0171] Table 2

[0172] Serial number Node cost value node 1 Cost_S4_30 / 90 S4 2 Cost_S4_50 / 90 S4 3 Cost_S4_30 / 60 S4 4 Cost_S4_50 / 60 S4 5 Cost_S5_50 / 90 S5 6 Cost_S5_50 / 60 S5

[0173] Finally, as shown in Table 2 above, the electronic device can determine Cost_S4_30 / 90 as the target node cost value from the cost value queue. Since the node cost value Cost_S4_30 / 90 corresponds to node S4, the electronic device determines that node S4 is the node to be traversed, and node S4 is not the end point. Therefore, the electronic device takes node S4 as the target node and adds the target node information of node S4 to the node list, and then traverses the neighboring nodes S1, S2 and S6 of node S4.

[0174] In some embodiments, the electronic device determines whether the node status information of the node to be traversed includes the charging information of the target node. If it includes the charging information of the target node, the target node information of the target node is updated according to the charging information and the step of determining whether the node to be traversed is the end point is entered. If it does not include the charging information of the target node, the step of determining whether the node to be traversed is the end point is entered directly.

[0175] For example, as mentioned above, the node to be traversed is S4, and the node status information of the node to be traversed S4 includes the charging information of the target node S2, that is, charging 30kWh in a 90kW charger. Therefore, the electronic device needs to update the target node information of the target node S2 with the charging information of the target node S2 and determine whether the node to be traversed S4 is the end point.

[0176] In order to further elaborate on the traversal process of the node information network provided by the embodiment of the present invention, Figure 2 To elaborate.

[0177] First, the electronic device initializes the node status information of the starting point A. After the initialization is completed, the node status information of the starting point A is obtained, the node status information is substituted into the objective function, the node cost value of the starting point A is obtained, and the node cost value is added to the cost value queue. Since the cost value queue only has the node cost value of the starting point A and the node cost value Cost_A of the starting point A is 0, the electronic device determines that the starting point A is the node to be traversed, and the starting point A is not the end point. Therefore, the electronic device takes the starting point A as the target node and adds the target node information of the starting point A (such as the location information of the starting point A) to the node list. In addition, the electronic device will also record the node status information of the starting point A (E_A, tc_A, mc_A, d_A, R_A).

[0178] Next, the electronic device determines that the remaining power of the starting point A is E_A, and traverses the neighboring nodes S1 and S2 of the starting point A with the starting point A as the current target node. The power consumption of the electric vehicle traveling from the starting point A to the neighboring node S1 is E_AS1, and the power consumption of the electric vehicle traveling from the starting point A to the neighboring node S2 is E_AS2. E_A is greater than E_AS1 and E_AS2. The electronic device then updates the node status information of the neighboring nodes S1 and S2 respectively and inputs them into the objective function, and determines that the node cost value of the neighboring node S2 is the smallest, and the neighboring node S2 is not the end point. Therefore, the electronic device can determine that the neighboring node S2 is the target node and add the target node information of the neighboring node S2 to the node list. In addition, the electronic device will also record the node status information of the neighboring node S2 (E_S2, tc_S2, mc_S2, d_S2, R_S2).

[0179] Next, the electronic device determines that the remaining power of node S2 is E_S2, and uses node S2 as the current target node to traverse the neighboring nodes A, S4 and S5 of node S2. Since neighboring node A has been traversed, the electronic device will traverse neighboring nodes S4 and S5. The power consumption of the electric vehicle traveling from node S2 to neighboring node S4 is E_S2S4, and the power consumption of the electric vehicle traveling from node S2 to neighboring node S5 is E_S2S5. E_S2 is less than E_S2S4 and E_S2S5. Since node S2 is a rechargeable node, the electronic device performs various charging attempts and updates the node status information of neighboring nodes S4 and S5, and substitutes the updated node status information into the objective function. The node cost values corresponding to neighboring nodes S4 and S5 are obtained. The electric vehicle travels from node S2 to neighboring node S4, and the node cost value generated by charging 30kWh at 90kW at node S2 is the smallest, and neighboring node S4 is not the end point. Therefore, the electronic device can determine that neighboring node S4 is the target node and add the target node information of neighboring node S4 to the node list. In addition, the electronic device will also record the node status information of neighboring node S4 (E_S4, tc_S4, mc_S4, d_S4, R_S4). Since the node status information contains the charging information of charging 20kWh at 90kW at node S2, the electronic device will also update the target node information of node S2 according to this charging information.

[0180] Next, the electronic device determines that the remaining power of node S4 is E_S4, and takes node S4 as the current target node to traverse the neighboring nodes S1, S2 and S6 of node S4. Since the neighboring node S2 has been traversed, the electronic device will traverse the neighboring nodes S1 and S6. The power consumption of the electric vehicle traveling from node S4 to the neighboring node S1 is E_S4S1, and the power consumption of the electric vehicle traveling from node S4 to the neighboring node S6 is E_S4S6. E_S4 is greater than E_S4S1 and E_S4S6. The electronic device updates the node status information of the neighboring nodes S1 and S6 respectively and then inputs them into the objective function, and determines that the node cost value of the neighboring node S6 is the smallest, and the neighboring node S6 is not the end point. Therefore, the electronic device can determine the neighboring node S6 as the target node and add the target node information of the neighboring node S6 to the node list. In addition, the electronic device will also record the node status information of the neighboring node S6 (E_S6, tc_S6, mc_S6, d_S6, R_S6).

[0181] Next, the electronic device determines that the remaining power of node S6 is E_S6, and uses node S6 as the current target node to traverse the neighboring nodes S3, S4 and S8 of node S6. Since the neighboring node S4 has been traversed, the neighboring node S3 and the target nodes A, S2, S4 and S6 are not on any candidate driving path. Therefore, the electronic device only needs to traverse the neighboring node S8. The power consumption of the electric vehicle from node S6 to neighboring node S8 is E_S6S4. Since E_S6 is less than E_S2S4 and node S6 is a rechargeable node, the electronic device performs various charging attempts and updates the node status information of each group of neighboring node S8, and updates the updated node status information. Substituting into the objective function, we obtain the node cost value corresponding to each set of node status information of the neighboring node S8. Assuming that the node cost value generated by charging 30kWh at 70kW is the smallest, and the neighboring node S8 is not the end point, the electronic device can determine that the neighboring node S8 is the target node and add the target node information of the neighboring node S8 to the node list. In addition, the electronic device will also record the node status information of the neighboring node S8 (E_S8, tc_S8, mc_S8, d_S8, R_S8). Since the node status information contains the charging information of charging 20kWh at 70kW at node S6, the electronic device will also update the target node information of node S6 based on this charging information.

[0182] Finally, the electronic device determines that the remaining power of node S8 is E_S8, and uses node S8 as the current target node to traverse the neighboring nodes S6 and B of node S8. Since the neighboring node S6 has been traversed, the electronic device only needs to traverse the neighboring node B. The power consumption of the electric vehicle traveling from node S8 to the neighboring node B is E_S8B. Assuming that E_S8 is less than E_S8B, since node S8 is a rechargeable node, the electronic device performs various charging attempts and updates each group of node status information of the neighboring node B, and substitutes the updated node status information into the objective function to obtain the node cost value corresponding to each group of node status information of the neighboring node B. Assuming that 20kWh is charged by 110kW The generated node cost value is the smallest, and neighbor node B is the end point, so the electronic device can determine neighbor node B as the target node and add the target node information of neighbor node B to the node list. In addition, the electronic device will also record the node status information (E_B, tc_B, mc_B, d_B, R_B) of neighbor node B. Since the node status information includes the charging information of charging 20kWh at 110kW at node S8, the electronic device will also update the target node information of node S8 based on this charging information. Since the traversal has ended at this time, the electronic device can generate a path planning scheme for the electric vehicle based on the target node information of each target node in the node list. For example, the path planning scheme is as follows:

[0183]

[0184]

[0185] It can be seen from the above path planning scheme that the total time taken for the electric vehicle to travel from starting point A to end point B is 9 hours, the total charging cost is 81 yuan, the total driving distance is 580 kilometers, and the target driving path is starting point A, nodes S2, S4, S6, S8 and end point B, wherein the target node information of starting point A includes the location information of starting point A, the target node information of node S2 includes the location information and charging information of node S2, and the charging information is charging 30kWh through a 90kW charger, the target node information of node S4 includes the location information of node S4, the target node information of node S6 includes the location information and charging information of node S6, and the charging information is charging 20kWh through a 70kW charger, and the target node information of node S8 includes the location information and charging information of node S8, and the charging information is charging 20kWh through a 110kW charger.

[0186] As another aspect of an embodiment of the present invention, an apparatus for generating a path planning solution is provided. The apparatus for generating a path planning solution may be a software module comprising a plurality of instructions stored in a memory, which a processor may access and execute to execute the instructions, thereby completing the path planning solution generation method described in each of the above embodiments.

[0187] In some embodiments, the path planning solution generation device can be constructed by hardware devices. For example, the path planning solution generation device can be constructed by one or more chips, and the chips can work in coordination with each other to complete the path planning solution generation method described in each of the above embodiments. For another example, the path planning solution generation device can also be constructed by a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a single-chip microcomputer, an ARM (Acorn RISC Machine), a programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination of these components.

[0188] In some embodiments, see Figure 6 The transmission path planning scheme generation 600 provided in the embodiment of the present invention includes an acquisition module 601, a construction module 602, a construction module 603, a traversal module 604 and a generation module 605.

[0189] The acquisition module 601 is used to obtain the starting point information, end point information and node information of each candidate node of the possible route from the starting point to the end point of the electric vehicle. The construction module 602 is used to construct a node information network based on the starting point information, end point information and node information. The node information network includes the starting point, end point and candidate nodes. The construction module 603 is used to construct an objective function based on multiple preset decision variables. The traversal module 604 is used to traverse each node of the node information network according to the objective function to obtain the target node information of each target node. The generation module 605 is used to generate a path planning plan for the electric vehicle based on the target node information.

[0190] In some embodiments, see Figure 7 The traversal module 604 includes a query unit 6041 , a calculation unit 6042 , a selection unit 6043 and an acquisition unit 6044 .

[0191] The acquisition unit 6041 is used to start from the starting point and query the node status information of each node in the node information network one by one. The calculation unit 6042 is used to calculate the node cost value of each candidate node based on the node status information and the objective function. The selection unit 6043 is used to select the target node from the candidate nodes in the node information network based on the node cost value. The acquisition unit 6044 is used to obtain the target node information of the target node.

[0192] In some embodiments, the selection unit 6043 is specifically used to: generate a cost value queue based on the node cost value, determine the target node cost value based on the cost value queue, determine the node to be traversed based on the target node cost value, judge whether the node to be traversed is the end point, if it is not the end point, take the node to be traversed as the target node and add the target node information of the target node to the node list, determine the remaining power of the electric vehicle when it travels to the current target node, select the target neighbor node of the current target node and calculate the power consumption of the electric vehicle from the current target node to the target neighbor node, traverse the neighbor node according to the remaining power and the power consumption, obtain the target node information, if it is the end point, take the node to be traversed as the target node and add the target node information of the target node to the node list, and generate a path planning plan for the electric vehicle based on the target node information of each target node in the node list.

[0193] It should be noted that the aforementioned path planning scheme generation device can execute the path planning scheme generation method provided in the embodiments of the present invention and has the corresponding functional modules and beneficial effects of the execution method. For technical details not fully described in the embodiments of the path planning scheme generation device, please refer to the path planning scheme generation method provided in the embodiments of the present invention.

[0194] See also Figure 8 , Figure 8The present invention provides a hardware structure diagram of an electronic device. Figure 8 As shown, the electronic device 800 includes one or more processors 801 and a memory 802. Figure 8 A processor 801 is taken as an example.

[0195] The processor 801 is configured to support the computer device in executing the corresponding functions of the method in the above method embodiment. The processor 801 can be a central processing unit (CPU), a network processor (NP), a hardware chip, or any combination thereof. The above hardware chip can be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The above PLD can be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0196] Memory 802 is used to store program code. Memory 802 may include volatile memory (VM), such as random access memory (RAM); non-volatile memory (NVM), such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid-state drive (SSD); or a combination of the aforementioned types of memory.

[0197] Memory 802 can be used to store non-volatile software programs, non-volatile computer executable programs, and modules, such as the program instructions / modules corresponding to the path planning solution generation method in the embodiments of the present invention. Processor 801 executes the non-volatile software programs, instructions, and modules stored in memory 802 to perform the various functional applications and data processing of the path planning solution generation method and path planning solution generation device, thereby implementing the functions of the various modules or units of the path planning solution generation method and path planning solution generation device provided in the above-mentioned method embodiments.

[0198] Memory 802 may include a program storage area and a data storage area. The program storage area may store an operating system and application programs required for at least one function. The data storage area may store data generated by the path planning solution generation device. In some embodiments, memory 802 may optionally include a memory remote from the processor, and such remote memory may be connected to the output power scheduling device via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0199] The one or more modules are stored in the memory 802. When executed by the one or more processors 801, the path planning scheme generation method in any of the above method embodiments is executed, for example, the method steps described in the above method embodiments are executed to realize the functions of the modules described in the above device embodiments.

[0200] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. The computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the method described in the above embodiment.

[0201] It will be understood by those skilled in the art that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0202] Finally, it should be noted that the present invention can be implemented in many different forms and is not limited to the embodiments described in this specification. These embodiments should not be construed as additional limitations on the content of the present invention. The purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of the present invention. Furthermore, under the principles of the present invention, the above-mentioned technical features can be further combined with each other, and there are many other variations of the different aspects of the present invention as described above, all of which are considered to be within the scope of the present invention. Furthermore, it is apparent to those skilled in the art that improvements or modifications can be made based on the above description, and all such improvements and modifications should fall within the scope of protection of the appended claims.

Claims

1. A path planning scheme generation method, characterized in that: include: Obtaining the starting point information, the end point information, and the node information of each candidate node on the possible path from the starting point to the end point of the electric vehicle; Constructing a node information network according to the starting point information, the end point information and the node information, wherein the node information network includes a starting point, an end point and candidate nodes; Construct an objective function based on multiple preset decision variables; Traversing each node of the node information network according to the target function to obtain target node information of each target node; A path planning scheme for the electric vehicle is generated according to the target node information.

2. The path planning scheme generation method according to claim 1, characterized in that: Traversing each node of the node information network according to the objective function to obtain target node information of each target node includes: Starting from the starting point, querying node status information of each node in the node information network one by one; calculating a node cost value of each candidate node according to the node status information and the objective function; A target node is selected from each candidate node in the node information network according to the node cost value, and target node information of the target node is obtained.

3. The path planning scheme generation method according to claim 2, characterized in that: Each node includes at least two neighboring nodes, and selecting a target node from candidate nodes in the node information network according to the node cost value includes: Adding the node cost value to a cost value queue; Determine the target node cost value according to the cost value queue; Determine the node to be traversed according to the target node cost value; Determine whether the node to be traversed is an end point; If it is not the end point, the node to be traversed is taken as the target node and the target node information of the target node is added to the node list, the remaining power of the electric vehicle when it travels to the current target node is determined, the target neighbor node of the current target node is selected and the power consumption of the electric vehicle when traveling from the current target node to the target neighbor node is determined, and the target node is selected from each candidate node of the node information network according to the remaining power and the power consumption; If it is the end point, the node to be traversed is used as the target node and the target node information of the target node is added to the node list, and a path planning scheme for the electric vehicle is generated according to the target node information of each target node in the node list.

4. The path planning scheme generation method according to claim 3, characterized in that: The cost value queue includes node cost values arranged in sequence, each node cost value corresponding to a node, and before determining whether the node to be traversed is an end point, further comprising: Determine whether the node to be traversed exists in the node list; If it exists in the node list, discard the target node cost value, re-determine the target node cost value according to the cost value queue and return to the step of determining the node to be traversed according to the target node cost value; If it does not exist in the node list, the process proceeds to the step of determining whether the node to be traversed is the end point.

5. The path planning scheme generation method according to claim 3, characterized in that: Before determining whether the node to be traversed is an end point, the method further includes: generating a plurality of candidate driving paths based on the node information network; Determine whether the node to be traversed and the target node in the node list are on a candidate driving path; If it is on a candidate driving path, then enter the step of determining whether the node to be traversed is the end point; If it is not on a candidate driving path, the target node cost value is discarded, the target node cost value is re-determined according to the cost value queue, and the process returns to the step of determining the node to be traversed according to the cost value queue.

6. The path planning scheme generation method according to claim 3, characterized in that: The node information of each node includes the driving distance and driving time of the node to each neighboring node, the node information network includes rechargeable nodes, and the target node is selected from each candidate node in the node information network according to the remaining power and the consumed power: Determining whether the remaining power is greater than the consumed power; If the power consumption is greater than the power consumption, the driving distance and driving time from the current target node to each neighboring node are obtained, the node status information of each neighboring node is determined based on the driving distance, the driving time, and the node status information of the current target node, each neighboring node is used as a node to be traversed, and the process returns to the step of calculating the node cost value of each node to be traversed based on the node status information and the objective function; If it is not greater than the power consumption, determining whether the current target node is a rechargeable node; If the node is rechargeable, determining a target charging capacity, adding the remaining capacity to the target charging capacity to obtain a target remaining capacity, and selecting a target node from each candidate node in the node information network according to the target remaining capacity; If it is a non-rechargeable node, the target neighbor node is skipped, a target neighbor node of the current target node is reselected, and the process returns to the step of determining the power consumption of the electric vehicle when traveling from the current target node to the target neighbor node.

7. The path planning scheme generation method according to claim 6, characterized in that: The node information of the rechargeable node further includes the output power and charging price of at least one charger, and the selecting of a target node from each candidate node of the node information network according to the target remaining power includes: Obtaining the battery capacity of an electric vehicle; Determining whether the target remaining power is greater than the consumed power and less than the battery capacity; If the target power consumption is greater than the power consumption and less than the battery capacity, the charging fee is calculated according to the target power consumption and the charging price, the charging time is calculated according to the target power consumption and the output power, and the node status information of the target neighbor node is determined according to the driving time, the driving distance, the charging fee, the charging time, and the node status information of the current target node, the target neighbor node is used as a candidate node, and the process returns to the step of calculating the node cost value of each candidate node according to the node status information and the objective function; If it is not greater than the consumed power or not less than the battery capacity, the target charging power is re-determined and the process returns to the step of adding the target charging power to the remaining power.

8. The path planning scheme generation method according to claim 7, characterized in that: Before determining whether the node to be traversed is an end point, the method further includes: Determining whether the node status information of the node to be traversed includes charging information of the target node; If the charging information of the target node is included, then the target node information of the target node is updated according to the charging information and the process proceeds to the step of determining whether the node to be traversed is the end point; If the charging information of the target node is not included, the process directly proceeds to the step of determining whether the node to be traversed is the end point.

9. The path planning scheme generating method according to any one of claims 1 to 8, characterized in that: When the candidate node is a rechargeable node, the node information of the rechargeable node includes reservation support status, the preset decision variables include cumulative consumption time, cumulative charging cost, cumulative driving distance and reservation support status, and the objective function is: Cost=α*time_cost+β*money_cost+γ*distance-c*reservations Among them, Cost is the cost value, α is the time weight, time_cost is the cumulative consumption time, β is the cost weight, money_cost is the cumulative charging cost, γ is the distance weight, distance is the cumulative driving distance, c is the preset coefficient, and reservations is the reservation support status. If reservation is supported, the value of reservations is the first preset value. If reservation is not supported, the value of reservations is the second preset value.

10. An electronic device, characterized in that: It includes a memory and a processor, the memory is connected to the processor, and the processor is used to execute one or more computer programs stored in the memory. When the processor executes the one or more computer programs, the electronic device implements the path planning solution generation method as described in any one of claims 1 to 9.

11. A storage medium, characterized in that: The storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor, the processor executes the path planning solution generating method according to any one of claims 1 to 9.

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