Vehicle path planning method and device, electronic equipment and storage medium

Abstract

The invention discloses a vehicle path planning method and device, electronic equipment and a storage medium. The method comprises the steps that in response to a received driving path generation instruction, position information and road condition parameter information of a vehicle are obtained, and the position information comprises the starting position of the vehicle and the target position where the vehicle needs to reach; constructing a topological structure diagram of the driving path based on the road condition parameter information, the initial position and the target position; and searching the topological structure diagram by using a target search model to obtain a driving path, the constraint conditions of the target search model including a battery power consumption constraint condition and a battery capacity constraint condition. According to the invention, the technical problem of low economical efficiency and feasibility of vehicle path planning due to the fact that the battery capacity of the vehicle and energy recovery in the vehicle driving process are not considered in the prior art is solved.

Description

Technical Field

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

[0002] In modern urban traffic, vehicle navigation systems play a vital role. They provide drivers with the best route from the starting point to the destination through advanced algorithms. However, existing technologies have the following main problems:

[0003] 1. Inaccurate energy consumption estimation: Since the energy consumption of pure electric vehicles is affected by many factors, including the vehicle's own conditions, road conditions, driving behavior, etc., existing technologies are difficult to accurately estimate the energy consumption of different routes;

[0004] 2. Lack of consideration of battery capacity and energy recovery: Existing path planning methods usually do not fully consider the maximum battery capacity and energy recovery effect of pure electric vehicles, which are crucial to the endurance and route selection of pure electric vehicles;

[0005] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0006] Embodiments of the present invention provide a vehicle path planning method, device, electronic device and storage medium to at least solve the technical problem in the related art that the economy and feasibility of vehicle path planning are low due to failure to consider the vehicle battery capacity and energy recovery during vehicle driving.

[0007] According to one embodiment of the present invention, a vehicle path planning method is provided, comprising: in response to receiving a driving path generation instruction, obtaining the vehicle's position information and road condition parameter information, wherein the position information includes the vehicle's starting position and the vehicle's target position; constructing a topological structure diagram of the driving path based on the road condition parameter information, the starting position and the target position; searching the topological structure diagram using a target search model to obtain the driving path, wherein the constraints of the target search model include a battery power consumption constraint and a battery capacity constraint.

[0008] Optionally, constructing a topological structure graph of a driving route based on road condition parameter information, a starting position and a target position includes: determining a selection range of the topological structure graph based on the starting position and the target position; determining node information of the topological structure graph based on road unit information and road length information in the road condition parameter information; determining the topological structure graph based on target energy loss information and node information between road units in the road condition parameter information, wherein the topological structure graph includes connection relationships and weight information between nodes.

[0009] Optionally, determining the weight information of the topological structure graph includes: obtaining height information of road units, distance information between road units and vehicle speed information; determining potential energy information between road units based on the height information and vehicle mass information; determining target energy loss information between road units based on the potential energy information, distance information and vehicle speed information; and determining weight information based on the target energy loss information.

[0010] Optionally, determining the target energy loss information between road units based on potential energy information, distance information and vehicle speed information includes: determining the third energy loss information based on the sum of the first energy loss information, the second energy loss information and the potential energy information, wherein the first energy loss information is determined based on the distance information, the mass information and the friction coefficient, and is used to represent the energy lost by the vehicle to overcome rolling resistance, and the second energy loss information is determined based on the distance information, the vehicle speed information and the air resistance coefficient, and is used to represent the energy lost by the vehicle to overcome air resistance; in response to the third energy loss information being greater than a preset threshold, determining the target energy loss information based on the first energy conversion factor of the vehicle and the third energy loss information; in response to the third energy loss information being less than or equal to the preset threshold, determining the target energy loss information based on the second energy conversion factor of the vehicle and the third energy loss information.

[0011] Optionally, the target search model is used to search the topological structure graph to obtain the driving path, including: constructing an energy loss objective function based on the estimated cost and the actual cost, wherein the estimated cost is determined based on the distance between the node to be visited and the target position, and the actual cost is determined based on the weight of the starting position and the node to be visited; constructing a target constraint relationship set of the energy loss objective function based on the initial power information and battery capacity information of the battery; and using the target search model to search the topological structure graph based on the target constraint relationship set and the energy loss objective function to obtain the driving path.

[0012] Optionally, based on the initial power information and battery capacity information of the battery, constructing a target constraint relationship set for the energy loss objective function includes: constructing the battery power constraint conditions of the driving path based on the weights and initial power information in the topological structure diagram; constructing the battery capacity constraint conditions of the driving path based on the difference between the weights, initial power information and battery capacity information in the topological structure diagram; constructing a target constraint relationship set based on the battery power constraint conditions and the battery capacity constraint conditions.

[0013] Optionally, a target search model is used to search the topological structure graph based on a target constraint relationship set and an energy loss target function to obtain a driving path including: selecting a node to be visited from a first node list based on an energy loss target function, wherein the first node list is determined based on node information in the topological structure graph; in response to the node to be visited satisfying the target constraint relationship set, placing the node to be visited into a node set that is a driving path; removing the node to be visited from the first node list to obtain a second node list; selecting a node to be visited again from the second node list based on the energy loss target function, and determining whether the node to be visited satisfies the target constraint relationship set, until the node to be visited is a node corresponding to the target position.

[0014] According to one embodiment of the present invention, a vehicle path planning device is also provided, including: an acquisition module, used to obtain the vehicle's location information, battery information, and road condition parameter and road structure information in response to receiving a driving path generation instruction, wherein the battery location information includes the vehicle's starting position and the vehicle's target position to be reached; a construction module, used to construct a topological structure diagram of the driving path based on the road condition parameter information, the starting position and the target position; a determination module, used to search the topological structure diagram using a target search model to obtain the driving path, wherein the constraints of the target search model include battery power consumption constraints and battery capacity constraints.

[0015] Optionally, the determination module is also used to: determine the selection range of the topological structure diagram based on the starting position and the target position; determine the node information of the topological structure diagram based on the road unit information and road length information in the road condition parameter information; determine the topological structure diagram based on the target energy loss information and node information between road units in the road condition parameter information, wherein the topological structure diagram includes the connection relationship and weight information between nodes.

[0016] Optionally, the acquisition module is also used to acquire the height information of the road unit, the distance information between the road units and the vehicle travel speed information; the determination module is also used to: determine the potential energy information between the road units based on the height information and the mass information of the vehicle; determine the target energy loss information between the road units based on the potential energy information, the distance information and the vehicle travel speed information; determine the weight information based on the target energy loss information.

[0017] Optionally, the determination module is also used to: determine third energy loss information based on the sum of first energy loss information, second energy loss information and potential energy information, wherein the first energy loss information is determined based on distance information, mass information and friction coefficient, and is used to represent the energy lost by the vehicle to overcome rolling resistance, and the second energy loss information is determined based on distance information, vehicle speed information and air resistance coefficient, and is used to represent the energy lost by the vehicle to overcome air resistance; in response to the third energy loss information being greater than a preset threshold, determine target energy loss information based on the vehicle's first energy conversion factor and the third energy loss information; in response to the third energy loss information being less than or equal to a preset threshold, determine the target energy loss information based on the vehicle's second energy conversion factor and the third energy loss information.

[0018] Optionally, the construction module is also used to: construct an energy loss objective function based on the estimated cost and the actual cost, wherein the estimated cost is determined based on the distance between the node to be visited and the target position, and the actual cost is determined based on the starting position and the weight of the node to be visited; construct a target constraint relationship set of the energy loss objective function based on the initial power information and battery capacity information of the battery; the determination module is also used to use the target search model to search the topological structure graph based on the target constraint relationship set and the energy loss objective function to obtain a driving path.

[0019] Optionally, the construction module is also used to: construct battery power constraints for the driving path based on the weights and initial power information in the topological structure diagram; construct battery capacity constraints for the driving path based on the difference between the weights, initial power information and battery capacity information in the topological structure diagram; and construct a target constraint relationship set based on the battery power constraints and battery capacity constraints.

[0020] Optionally, the determination module is also used to: select a node to be visited from a first node list based on an energy loss objective function, wherein the first node list is determined based on node information in a topological structure diagram; in response to the node to be visited satisfying a target constraint relationship set, place the node to be visited into a node set that is a driving path; remove the node to be visited from the first node list to obtain a second node list; select a node to be visited again from the second node list based on the energy loss objective function, and determine whether the node to be visited satisfies the target constraint relationship set, until the node to be visited is a node corresponding to the target position.

[0021] According to one embodiment of the present invention, there is also provided an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein the above-mentioned vehicle path planning method is executed when the program is running.

[0022] According to one embodiment of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein when the executable program is running, the device where the storage medium is located is controlled to execute the above-mentioned vehicle path planning method.

[0023] According to one embodiment of the present invention, a computer program product is also provided, including a computer program, and the computer program implements the above-mentioned vehicle path planning method when executed by a processor.

[0024] In an embodiment of the present invention, in response to receiving a driving route generation instruction, the vehicle's position information and road condition parameter information are obtained, a topological structure diagram of the driving route is constructed based on the road condition parameter information, the starting position and the target position, and the topological structure diagram is searched using a target search model to obtain the driving route, thereby achieving the purpose of timely planning a driving route with low energy consumption for the vehicle by comprehensively considering the vehicle battery capacity and the energy recovery factors during vehicle driving, thereby achieving the technical effect of improving the economy and feasibility of vehicle route planning, and further solving the technical problem in the related art that the economy and feasibility of vehicle route planning are low due to failure to consider the vehicle battery capacity and the energy recovery during vehicle driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0026] Figure 1 is a flow chart of a vehicle path planning method according to one embodiment of the present invention;

[0027] Figure 2 is a flow chart of another vehicle path planning method according to one embodiment of the present invention;

[0028] Figure 3 It is a structural block diagram of a vehicle path planning device according to one embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] According to an embodiment of the present invention, a vehicle path planning method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0032] The method embodiment can be executed in an electronic device or a similar computing device including a memory and a processor. Taking running on a vehicle terminal as an example, the vehicle terminal may include one or more processors (the processor may include but is not limited to a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processing (DSP) chip, a microcontroller unit (MCU), a programmable logic device (Field Programmable Gate Array, FPGA), a neural network processor (Neural-network Processor Unit, NPU), a tensor processor (Tensor Processing Unit, TPU), an artificial intelligence (Artificial Intelligence, AI) type processor, etc.) and a memory for storing data. Optionally, the above-mentioned vehicle terminal may also include a transmission device, an input and output device, and a display device for communication functions. It can be understood by those of ordinary skill in the art that the above-mentioned structural description is only for illustration and does not limit the structure of the above-mentioned vehicle terminal. For example, the vehicle terminal may also include more or fewer components than the above-mentioned structural description, or have a configuration different from the above-mentioned structural description.

[0033] The memory can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the vehicle path planning method in the embodiment of the present invention. The processor executes various functional applications and data processing by running the computer program stored in the memory, that is, the above-mentioned vehicle path planning method is realized. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0034] The transmission device is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device can be a radio frequency (Radio Frequency, referred to as RF) module, which is used to communicate with the Internet wirelessly.

[0035] The display device may be, for example, a touch screen type liquid crystal display (LCD) and a touch display (also referred to as a "touch screen" or "touch display screen"). The liquid crystal display may enable a user to interact with a user interface of a mobile terminal. In some embodiments, the mobile terminal has a graphical user interface (GUI), and a user may interact with the GUI by finger contacts and / or gestures on a touch-sensitive surface, wherein the human-computer interaction functions here may optionally include the following interactions: creating web pages, drawing, word processing, making electronic documents, games, video conferencing, instant messaging, sending and receiving emails, call interfaces, playing digital videos, playing digital music and / or web browsing, etc. The executable instructions for executing the above human-computer interaction functions are configured / stored in a computer program product or readable storage medium executable by one or more processors.

[0036] Figure 1 is a flow chart of a vehicle path planning method according to one embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0037] Step S10, in response to receiving the driving route generation instruction, obtaining the vehicle's position information and road condition parameter information, wherein the position information includes the vehicle's starting position and the vehicle's target position;

[0038] In step 10, the above vehicle is used to characterize a pure electric vehicle.

[0039] The above vehicle position information is used to represent the vehicle's starting position and the vehicle's target position.

[0040] The above-mentioned road condition parameter information is used to characterize the road network structure information and traffic flow information, for example, the length information of each road, the slope information, the location information of the intersection, and the vehicle speed information of each road section.

[0041] Specifically, the driver can input the target location of the vehicle through the mobile phone or the large screen of the vehicle's central control. The starting location of the vehicle can be obtained through the vehicle's global positioning system (GPS), vehicle navigation system or roadside intelligent equipment; road condition parameter information can be obtained through digital map data, geographic information system, etc. The speed information of each road section can be obtained by the traffic management department publishing real-time traffic flow data in real time, accessing the application programming interface (API) of the map service, etc.

[0042] Step S12, constructing a topological structure diagram of the driving route based on the road condition parameter information, the starting position and the target position;

[0043] Specifically, a driving route is constructed according to the road condition parameter information, the starting position and the target position, and a topological structure diagram of the driving route is constructed. The topological structure diagram is a directed graph structure, denoted as G = (V, E), where |V| = n represents the number of nodes in the topological structure diagram, and |E| = m represents the number of edges in the topological structure diagram. The nodes v∈V in the topological structure diagram include intersections, starting positions, target positions, and newly generated nodes when dividing road units. The edges e∈E in the topological structure diagram represent road units connecting two nodes.

[0044] Step S14, searching the topological structure diagram using the target search model to obtain a driving path, wherein the constraints of the target search model include battery power consumption constraints and battery capacity constraints.

[0045] In step S14, the target search model is used to use the improved A* algorithm to find a path with better energy consumption under the constraints of battery power consumption and battery capacity of the vehicle, and use the path as the driving path.

[0046] Based on the above steps S10 to S14, in response to receiving a driving route generation instruction, the vehicle's position information and road condition parameter information are obtained, a topological structure diagram of the driving route is constructed based on the road condition parameter information, the starting position and the target position, and the topological structure diagram is searched using a target search model to obtain the driving route, thereby achieving the purpose of timely planning a driving route with low energy consumption for the vehicle by comprehensively considering the vehicle battery capacity and the energy recovery factors during vehicle driving, thereby achieving the technical effect of improving the economy and feasibility of vehicle route planning, and further solving the technical problem in the related art that the economy and feasibility of vehicle route planning are low due to failure to consider the vehicle battery capacity and the energy recovery during vehicle driving.

[0047] Optionally, in step S12, constructing a topological structure diagram of the driving path based on the road condition parameter information, the starting position and the target position includes:

[0048] Step S121, determining a selection range of the topological structure diagram based on the starting position and the target position;

[0049] Specifically, a search radius or search distance is set according to the starting position and the target position, which will determine the selection range of the topological structure diagram. For example, a circle is drawn with the midpoint of the straight-line distance between the starting position and the target position as the center and the straight-line distance between the starting position and the target position as the diameter, and the area covered by the circle is used as the selection range of the topological structure diagram.

[0050] Step S122, determining node information of the topological structure diagram based on the road unit information and the road length information in the road condition parameter information;

[0051] Specifically, the road sections are divided with the road intersections, the vehicle starting position and the target position as nodes to obtain multiple road units. The road units are used as nodes of the topological structure diagram. In addition, for some road units with long road lengths and large road slope changes, they can be divided into shorter road units according to the road length or slope. At this time, new nodes will be newly generated, and the new nodes are also used as nodes of the topological structure diagram.

[0052] Step S123, determining a topological structure diagram based on the target energy loss information and node information between road units in the road condition parameter information, wherein the topological structure diagram includes connection relationships and weight information between nodes.

[0053] In step S123, the target energy loss information is used to characterize the energy that the vehicle needs to consume when traveling between two nodes, which is denoted as c(a, b).

[0054] Specifically, the branch information is determined according to the link relationship between the nodes, and the target energy loss information is used as the weight of the branch in the topological structure graph, thereby obtaining a directed weighted topological structure graph G=(V, E, c), where c represents the weight of the branch.

[0055] Based on the above steps S121 to S123, the selection range of the topological structure diagram is determined based on the starting position and the target position; the node information of the topological structure diagram is determined based on the road unit information and the road length information in the road condition parameter information; the topological structure diagram is determined based on the target energy loss information and the node information between the road units in the road condition parameter information, wherein the topological structure diagram includes the connection relationship and weight information between the nodes, which can ensure that the constructed topological structure diagram accurately reflects the characteristics and complexity of the actual road network, and uses the target energy loss information as the weight of the branch, which helps to select the driving path with the lowest energy consumption.

[0056] Optionally, in step S123, determining weight information in the topology diagram includes:

[0057] Step S1231, obtaining height information of the road unit, distance information between road units and vehicle travel speed information;

[0058] Specifically, when an electric vehicle travels from a low-height node to a high-height node, in addition to overcoming rolling resistance and air resistance, it also needs to convert part of the electrical energy into potential energy; and when an electric vehicle travels from a high-height node to a low-height node, it can use the vehicle's energy recovery mechanism to convert part of the potential energy into electrical energy for storage. It can be seen that when an electric vehicle travels from point A to point B and from point B to point A, the energy consumed when traveling from two different directions is different. Therefore, it is necessary to obtain the height information z of the road unit, the distance information l between the road units, and the vehicle driving speed information, and construct the topological structure diagram into a directed graph structure, where the vehicle driving speed used in the topological structure diagram is the average speed s of the vehicle traveling on the corresponding branch road.

[0059] Step S1232, determining potential energy information between road units based on the height information and the mass information of the vehicle;

[0060] Specifically, potential energy refers to the energy of an electric vehicle at a certain node due to its height. The potential energy of an electric vehicle at node a is represented by E P (z(a)).

[0061] Concrete, E P (z(a)) can be expressed as mg(z(a)), where m represents the total mass of the vehicle and g represents the acceleration due to gravity. When the vehicle moves from node a to node b, if z(a)≤z(b), the vehicle needs to consume some electrical energy to overcome the potential energy difference cP (a,b)=E P (z(b))-E P (z(a)); On the contrary, if z(b)≤z(a), then part of the potential energy difference c P (a,b)=E P (z(b))-E P (z(a)) will be recovered by the vehicle and stored in the battery, realizing the mutual conversion between potential energy and electrical energy.

[0062] Step S1233, determining target energy loss information between road units based on potential energy information, distance information and vehicle travel speed information;

[0063] Specifically, in addition to the conversion of the vehicle's electrical energy into potential energy, a portion of it is dissipated into the environment. L (a, b) represents the energy dissipated by the vehicle in order to overcome rolling resistance and air resistance during driving. L (a, b) is related to the distance information l between road units and the vehicle's travel speed s between road units. P (a, b) and loss energy c L (a, b) determines the target energy loss information c(a, b), where the calculation process of c(a, b) is shown in expression (1).

[0064] c(a,b)=c L (a,b)+c P (a,b) (1)

[0065] Where c(a,b) represents the target energy loss information of the vehicle from node a to node b; c P (a,b) represents the potential energy of the vehicle from node a to node b; c L (a,b) represents the loss energy of the vehicle from node a to node b.

[0066] Step S1234, determining weight information according to the target energy loss information.

[0067] Specifically, the value of c(a,b) is used as the branch (v a ,v b )∈E, which represents the energy consumed by the vehicle to drive from node a to node b.

[0068] Based on the above steps S1231 to S1234, the height information of the road unit, the distance information between the road units and the vehicle speed information are obtained; the potential energy information between the road units is determined based on the height information and the mass information of the vehicle; the target energy loss information between the road units is determined based on the potential energy information, the distance information and the vehicle speed information; the weight information is determined based on the target energy loss information, and the potential energy change and the energy recovery mechanism are included in the considerations of the path selection, so that the weight information is closer to the actual driving situation, and it can be ensured that the weight information can accurately reflect the energy consumption difference when the vehicle is traveling in different directions during path planning.

[0069] Optionally, in step S1233, determining target energy loss information between road units based on potential energy information, distance information and vehicle travel speed information includes:

[0070] Step S12331, determining third energy loss information based on the sum of the first energy loss information, the second energy loss information and the potential energy information, wherein the first energy loss information is determined based on the distance information, the mass information and the friction coefficient, and is used to represent the energy lost by the vehicle to overcome rolling resistance, and the second energy loss information is determined based on the distance information, the vehicle speed information and the air resistance coefficient, and is used to represent the energy lost by the vehicle to overcome air resistance;

[0071] Specifically, the first energy loss information c f (a, b) Based on distance information l(a, b), mass information m and friction coefficient f r Determine, where f r It is used to represent the friction coefficient between the vehicle and the road. The calculation process of the first energy loss information is shown in expression (2).

[0072] c f (a,b)=f r mgl(a,b) (2)

[0073] Among them, c f (a, b) represents the first energy loss information; f r represents the friction coefficient; l(a,b) represents the road length between node a and node b; m represents the mass information of the vehicle; g represents the acceleration of gravity.

[0074] Specifically, the second energy loss information c A (a, b) is based on distance information l(a, b), mass information m and air resistance coefficient c w The calculation process of the second energy loss information is shown in expression (3).

[0075]

[0076] Among them, c A (a, b) represents the second energy loss information; A represents the frontal area of ​​the vehicle; ρ represents the air density; c w represents the air resistance coefficient; s(a,b) represents the average speed of the vehicle between node a and node b; l(a,b) represents the length of the road between node a and node b.

[0077] Specifically, the third energy loss information c R The calculation process of (a, b) is shown in expression (4).

[0078]

[0079] Among them, c R (a, b) represents the third energy loss information of the vehicle from node a to node b; c P (a,b) The potential energy of the vehicle from node a to node b; f r mgl(a,b) represents the first energy loss information of the vehicle from node a to node b; Represents the second energy loss information of the vehicle from node a to node b.

[0080] Step S12332, in response to the third energy loss information being greater than a preset threshold, determining target energy loss information based on the first energy conversion factor of the vehicle and the third energy loss information;

[0081] Step S12333, in response to the third energy loss information being less than or equal to a preset threshold, determining target energy loss information based on the second energy conversion factor of the vehicle and the third energy loss information.

[0082] Specifically, when c R When (a,b)>0, the loss energy c L The calculation process of (a, b) is shown in expression (5).

[0083]

[0084] Among them, c L (a,b) The energy loss information of the vehicle from node a to node b; η c represents the first energy conversion factor, where η c ∈[0,1]; c R (a, b) represents the third energy loss information of the vehicle from node a to node b; c P (a,b) The potential energy of the vehicle from node a to node b.

[0085] Specifically, when c R When (a,b)≤0, the loss energy c LThe calculation process of (a, b) is shown in expression (6).

[0086] c L (a,b)=η r ·c R (a,b)-c P (a,b) (6)

[0087] Among them, c L (a,b) The energy loss information of the vehicle from node a to node b; η r represents the second energy conversion factor, where η r ∈[0,1]; c R (a, b) represents the third energy loss information of the vehicle from node a to node b; c P (a,b) The potential energy of the vehicle from node a to node b.

[0088] Specifically, the calculation process of determining the target energy loss information c(a, b) according to expressions (1), (5)-(6) is shown in expression (7).

[0089]

[0090] Where c(a,b) represents the target energy loss information of the vehicle from node a to node b; c L (a, b) The energy loss information of the vehicle from node a to node b; c P (a, b) The potential energy of the vehicle from node a to node b; c R (a, b) represents the third energy loss information of the vehicle from node a to node b.

[0091] Based on the above steps S12331 to S12333, the third energy loss information is determined based on the sum of the first energy loss information, the second energy loss information and the potential energy information; in response to the third energy loss information being greater than a preset threshold, the target energy loss information is determined based on the first energy conversion factor of the vehicle and the third energy loss information; in response to the third energy loss information being less than or equal to the preset threshold, the target energy loss information is determined based on the second energy conversion factor of the vehicle and the third energy loss information, taking into account a variety of energy loss factors, including rolling resistance, air resistance and potential energy changes, and the interaction between them. This meticulous calculation method makes the target energy loss information more comprehensive and accurate, thereby achieving the technical effect of improving the economy and feasibility of vehicle path planning, and further solving the technical problem in the related art of low economy and feasibility of vehicle path planning due to failure to consider vehicle battery capacity and energy recovery during vehicle driving.

[0092] Optionally, in step S14, the topological structure graph is searched using the target search model to obtain the driving path including:

[0093] Step S141, constructing an energy loss objective function based on the estimated cost and the actual cost, wherein the estimated cost is determined based on the distance between the node to be visited and the target location, and the actual cost is determined based on the weight of the starting location and the node to be visited;

[0094] In step S141, the estimated cost is as shown in expression (8).

[0095] h(u,t)=h L (u,t)+h π (u,t) (8)

[0096] Where u represents the node corresponding to the starting position; t represents the node corresponding to the target position; h(u,t) represents the estimated energy cost from u to t; h L (u,t)=E L (s min ,∥(u,t)∥,…),∥(u,t)∥ represents the straight-line distance from u to t, s min Indicates the minimum average speed between nodes; h π (u,t)=mgt-mgu=c P (u,t).

[0097] Specifically, the actual cost of a vehicle from u to t can be expressed as: c(u,t) = c L (u,t)+c P (u, t). Compared with h(u, t) = h L (u,t)+h π (u,t),h π (u,t)=c P (u, t), therefore, the difference between c(u, t) and h(u, t) is only in c L (u,t) and h L (u,t). Since, c L (u,t)=E L (l,s,…), the cost of the path increases monotonically with the average vehicle speed and is positively correlated with the distance between nodes, and s min ≤s, ∥(u,t)∥≤l, so h L (u,t)≤c L (u, t) holds true, so using h(u, t) as the heuristic function of the improved A* algorithm can ensure that the driving path from the starting position to the target position found by the improved A* algorithm is the path with the optimal energy consumption.

[0098] In step S141, the actual cost is determined based on the weights of the starting position and the node to be visited. For example, u represents the node corresponding to the starting position, w represents the node to be visited, and there is no other transit node between u and w, then the actual cost between u and w is c(u,w)

[0099] Specifically, the energy loss objective function is shown in expression (9).

[0100] G(u,t)=c(u,t)+h(u,t) (9)

[0101] Among them, c(u,t) represents the actual cost; h(u,t) represents the estimated cost.

[0102] Step S142, constructing a target constraint relationship set of an energy loss target function based on the initial power information and battery capacity information of the battery;

[0103] Specifically, when planning a route, the vehicle should exclude routes that cannot be completely passed after the initial power J is exhausted. In addition, when the vehicle recovers energy while going downhill, the energy recovered by the vehicle is related to the maximum capacity C of the vehicle battery. If the vehicle battery does not have additional capacity space, the vehicle cannot convert the recovered energy into electrical energy and store it in the battery. Therefore, in the process of searching for a driving route using the improved A* algorithm, the target constraint relationship set of the energy loss objective function is constructed based on the initial power information J and the battery capacity information C of the battery.

[0104] Step S143, using the target search model to search the topology graph based on the target constraint relationship set and the energy loss objective function to obtain a driving path.

[0105] Specifically, the improved A* algorithm is used to search the topological structure graph based on the target constraint relationship set and the energy loss objective function to obtain the driving path. That is, according to the created directed weighted topological structure graph G = (V, E, c), the vehicle's starting node s, the target node t, the vehicle's initial battery power J, and the vehicle's maximum battery capacity C, a path P in the path G is found so that the energy consumption of the path is minimized.

[0106] Based on the above steps S141 to S143, an energy loss objective function is constructed based on the estimated cost and the actual cost; based on the initial power information of the battery and the battery capacity information, a target constraint relationship set of the energy loss objective function is constructed; and the target search model is used to search the topological structure diagram based on the target constraint relationship set and the energy loss objective function to obtain a driving path, thereby achieving the purpose of timely planning a driving path with low energy consumption for the vehicle by comprehensively considering the vehicle battery capacity and the energy recovery factors during vehicle driving, thereby achieving the technical effect of improving the economy and feasibility of vehicle path planning, and further solving the technical problem of low economy and feasibility of vehicle path planning due to failure to consider the vehicle battery capacity and energy recovery during vehicle driving in the related technology.

[0107] Optionally, in step S142, based on the initial power information and battery capacity information of the battery, constructing a target constraint relationship set of the energy loss target function includes:

[0108] Step S1421, constructing battery power constraint conditions of the driving route based on the weights and initial power information in the topology diagram;

[0109] Specifically, suppose P = (v1, v2, ..., v k ) is a path from the starting node to the target node in the directed weighted topological structure graph G = (V, E, c), P i =(v1,v2,…,v i ) is a line of P with end point v i (i≤k) subpath, P k =P, then the path P k The cost is shown in expression (10).

[0110]

[0111] Among them, Δ k =c J (P k-1 )+c(v k-1 ,v k ); when Δ k >J, it means that the energy consumed by the vehicle when traveling along this path is greater than the initial charge of the vehicle, and this path is not feasible.

[0112] Step S1422, constructing a battery capacity constraint condition for the driving route based on the weights in the topology diagram, the initial power information, and the difference between the battery capacity information;

[0113] Specifically, consider the path P with the maximum capacity C of the vehicle battery k The cost is shown in expression (11).

[0114]

[0115] Among them, Δ k =c CJ (P k-1 )+c(v k-1 ,v k ). By J (P k ) is converted to c CJ (P k ), the cost of the path not only takes into account the initial charge J of the pure electric vehicle, but also the maximum capacity C of the pure electric vehicle battery. In addition, c CJ (P k ) eliminates the possible negative weights in the directed weighted topological structure graph G = (V, E, c), which enables s to use the path optimization method of the improved A* algorithm to find a driving path in the directed weighted topological structure graph that consumes the least energy from the starting node to the target node.

[0116] Step S1423: construct a target constraint relationship set based on the battery power constraint condition and the battery capacity constraint condition.

[0117] Based on the above steps S1421 to S1423, based on the weights and initial power information in the topological structure diagram, the battery power constraint of the driving path is constructed; based on the difference between the weights, initial power information and battery capacity information in the topological structure diagram, the battery capacity constraint of the driving path is constructed; based on the battery power constraint and the battery capacity constraint, a target constraint relationship set is constructed. By constructing the battery power constraint and the battery capacity constraint of the driving path, the energy range that the vehicle can consume during driving can be accurately limited, which ensures that the path planning algorithm can take into account the energy limitation of the vehicle when searching for the optimal path, and avoids planning a path that exceeds the energy capacity of the vehicle. In addition, by incorporating the battery power and battery capacity as constraints into the objective function, it can be ensured that the planned path is feasible in actual driving, which improves the reliability of path planning and reduces the risk of driving interruption or failure due to insufficient energy. Finally, by converting the battery power and battery capacity constraints into part of the path cost and using the improved A* algorithm for path optimization, the optimal or suboptimal path that meets the energy constraint can be efficiently found, which improves the efficiency of path planning and reduces computing time and resource consumption.

[0118] Optionally, in step S143, the target search model is used to search the topology diagram based on the target constraint relationship set and the energy loss objective function, and the driving path obtained includes:

[0119] Step S1431, selecting a node to be visited from a first node list based on an energy loss objective function, wherein the first node list is determined based on node information in a topological structure diagram;

[0120] Step S1432, in response to the to-be-visited node satisfying the target constraint relationship set, the to-be-visited node is placed into the node set of the driving path;

[0121] Step S1433, remove the node to be visited from the first node list to obtain a second node list;

[0122] Step S1434, based on the energy loss objective function, a node to be visited is selected again from the second node list, and it is determined whether the node to be visited satisfies the target constraint relationship set, until the node to be visited is the node corresponding to the target position.

[0123] Specifically, first, a node to be visited is selected from the nodes in the topological structure diagram based on the energy loss objective function, wherein the node to be visited must be adjacent to the starting node and the function value of the energy loss objective function is minimized. Then, it is determined whether the node to be visited satisfies the target constraint relationship set. If the node to be visited satisfies the target constraint relationship set, the node to be visited is placed in the node set of the driving path, and at the same time, the node to be visited is removed from the first node list to obtain a second node list; then, based on the energy loss objective function, a node to be visited is selected from the second node list again, the node to be visited must be adjacent to the previous node to be visited, and the function value of the energy loss objective function is minimized, and it is determined again whether the node to be visited satisfies the target constraint relationship set. If the node to be visited satisfies the target constraint relationship set, the node to be visited is placed in the node set of the driving path, and at the same time, the node to be visited is removed from the second node list; the above search process is repeated until the node to be visited is the node corresponding to the target position, then the nodes selected each time form a driving path according to the selection order, and the driving path can both minimize the function value of the energy loss objective function and satisfy the target constraint relationship set.

[0124] Specifically, Table 1 is a pseudo code for searching the energy-optimal path using the improved A* algorithm.

[0125] Table 1 Pseudo code for searching the energy-optimal path using the improved A* algorithm

[0126]

[0127]

[0128] Based on the above steps S1431 to S1434, a node to be visited is selected from the first node list based on the energy loss objective function; in response to the node to be visited satisfying the target constraint relationship set, the node to be visited is placed in the node set of the driving path; the node to be visited is removed from the first node list to obtain a second node list; based on the energy loss objective function, a node to be visited is selected again from the second node list, and it is determined whether the node to be visited satisfies the target constraint relationship set, until the node to be visited is the node corresponding to the target position, the purpose of timely planning a driving path with low energy consumption for the vehicle by comprehensively considering the vehicle battery capacity and the energy recovery factors during the vehicle driving process, thereby achieving the technical effect of improving the economy and feasibility of vehicle path planning, and then solving the technical problem of low economy and feasibility of vehicle path planning due to failure to consider the vehicle battery capacity and energy recovery during the vehicle driving process in the related technology.

[0129] Figure 2 FIG. 1 is a flow chart of another vehicle path planning method according to one embodiment of the present invention. Figure 2 As shown, the vehicle path planning method mainly includes the following execution steps:

[0130] Step S201, in response to receiving a driving route generation instruction, obtaining vehicle position information and road condition parameter information, wherein the position information includes a starting position of the vehicle and a target position to be reached by the vehicle;

[0131] Step S202, determining a selection range of the topological structure diagram based on the starting position and the target position;

[0132] Step S203, determining node information of the topological structure diagram based on the road unit information and the road length information in the road condition parameter information;

[0133] Step S204, obtaining height information of the road unit, distance information between road units and vehicle travel speed information;

[0134] Step S205, determining potential energy information between road units based on the height information and the mass information of the vehicle;

[0135] Step S206, determining target energy loss information between road units based on potential energy information, distance information and vehicle travel speed information;

[0136] Step S207, determining weight information according to the target energy loss information;

[0137] Step S208, determining a topology diagram based on the weight information and the node information;

[0138] Step S209, constructing an energy loss objective function based on the estimated cost and the actual cost;

[0139] Step S210, constructing a target constraint relationship set of an energy loss target function based on the initial power information and battery capacity information of the battery;

[0140] Step S211, using the target search model to search the topology graph based on the target constraint relationship set and the energy loss objective function to obtain a driving path.

[0141] Based on the above steps S201 to S211, in response to receiving a driving route generation instruction, the vehicle's position information and road condition parameter information are obtained, a topological structure diagram of the driving route is constructed based on the road condition parameter information, the starting position and the target position, and the topological structure diagram is searched using a target search model to obtain the driving route, thereby achieving the purpose of timely planning a driving route with low energy consumption for the vehicle by comprehensively considering the vehicle battery capacity and the energy recovery factors during vehicle driving, thereby achieving the technical effect of improving the economy and feasibility of vehicle route planning, and further solving the technical problem in the related art that the economy and feasibility of vehicle route planning are low due to failure to consider the vehicle battery capacity and the energy recovery during vehicle driving.

[0142] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0143] In an embodiment of the present invention, a vehicle path planning device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0144] Figure 3 is a structural block diagram of a vehicle path planning device according to one embodiment of the present invention. Figure 3 As shown, the device comprises:

[0145] The acquisition module 301 is used to acquire the vehicle's location information, battery information, and road condition parameter and road structure information in response to receiving the driving route generation instruction, wherein the battery location information includes the vehicle's starting location and the vehicle's target location;

[0146] A construction module 302 is used to construct a topological structure diagram of the driving path based on the road condition parameter information, the starting position and the target position;

[0147] The determination module 303 is used to search the topology diagram using the target search model to obtain a driving path, wherein the constraint conditions of the target search model include a battery power consumption constraint condition and a battery capacity constraint condition.

[0148] Optionally, the determination module 303 is also used to: determine the selection range of the topological structure diagram based on the starting position and the target position; determine the node information of the topological structure diagram based on the road unit information and road length information in the road condition parameter information; determine the topological structure diagram based on the target energy loss information and node information between road units in the road condition parameter information, wherein the topological structure diagram includes the connection relationship and weight information between nodes.

[0149] Optionally, the acquisition module 301 is also used to obtain the height information of the road unit, the distance information between the road units and the vehicle speed information; the determination module 303 is also used to: determine the potential energy information between the road units based on the height information and the mass information of the vehicle; determine the target energy loss information between the road units based on the potential energy information, the distance information and the vehicle speed information; determine the weight information according to the target energy loss information.

[0150] Optionally, the determination module 303 is also used to: determine the third energy loss information based on the sum of the first energy loss information, the second energy loss information and the potential energy information, wherein the first energy loss information is determined based on the distance information, the mass information and the friction coefficient, and is used to represent the energy lost by the vehicle to overcome rolling resistance, and the second energy loss information is determined based on the distance information, the vehicle speed information and the air resistance coefficient, and is used to represent the energy lost by the vehicle to overcome air resistance; in response to the third energy loss information being greater than a preset threshold, determine the target energy loss information based on the first energy conversion factor of the vehicle and the third energy loss information; in response to the third energy loss information being less than or equal to the preset threshold, determine the target energy loss information based on the second energy conversion factor of the vehicle and the third energy loss information.

[0151] Optionally, the construction module 302 is also used to: construct an energy loss objective function based on the estimated cost and the actual cost, wherein the estimated cost is determined based on the distance between the node to be visited and the target position, and the actual cost is determined based on the starting position and the weight of the node to be visited; construct a target constraint relationship set of the energy loss objective function based on the initial power information and battery capacity information of the battery; the determination module 303 is also used to use the target search model to search the topological structure diagram based on the target constraint relationship set and the energy loss objective function to obtain a driving path.

[0152] Optionally, the construction module 302 is also used to: construct battery power constraints for the driving path based on the weights and initial power information in the topological structure diagram; construct battery capacity constraints for the driving path based on the difference between the weights, initial power information and battery capacity information in the topological structure diagram; and construct a target constraint relationship set based on the battery power constraints and battery capacity constraints.

[0153] Optionally, the determination module 303 is also used to: select a node to be visited from a first node list based on an energy loss objective function, wherein the first node list is determined based on node information in a topological structure diagram; in response to the node to be visited satisfying a target constraint relationship set, place the node to be visited into a node set that is a driving path; remove the node to be visited from the first node list to obtain a second node list; select a node to be visited again from the second node list based on the energy loss objective function, and determine whether the node to be visited satisfies the target constraint relationship set, until the node to be visited is a node corresponding to the target position.

[0154] It should be noted that the above modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0155] According to one embodiment of the present invention, there is also provided an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein the above-mentioned vehicle path planning method is executed when the program is running.

[0156] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0157] Step S1, in response to receiving a driving route generation instruction, obtaining vehicle position information and road condition parameter information, wherein the position information includes a starting position of the vehicle and a target position to be reached by the vehicle;

[0158] Step S2, constructing a topological structure diagram of the driving path based on the road condition parameter information, the starting position and the target position;

[0159] Step S3, searching the topological structure diagram using the target search model to obtain a driving path, wherein the constraints of the target search model include battery power consumption constraints and battery capacity constraints.

[0160] According to one embodiment of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein when the executable program is running, the device where the storage medium is located is controlled to execute the above-mentioned vehicle path planning method.

[0161] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0162] Step S1, in response to receiving a driving route generation instruction, obtaining vehicle position information and road condition parameter information, wherein the position information includes a starting position of the vehicle and a target position to be reached by the vehicle;

[0163] Step S2, constructing a topological structure diagram of the driving path based on the road condition parameter information, the starting position and the target position;

[0164] Step S3, searching the topological structure diagram using the target search model to obtain a driving path, wherein the constraints of the target search model include battery power consumption constraints and battery capacity constraints.

[0165] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store computer programs.

[0166] According to one embodiment of the present invention, a computer program product is also provided, including a computer program, and the computer program implements the above-mentioned vehicle path planning method when executed by a processor.

[0167] Optionally, in this embodiment, the computer program product may be configured as a computer program for executing the following steps:

[0168] Step S1, in response to receiving a driving route generation instruction, obtaining vehicle position information and road condition parameter information, wherein the position information includes a starting position of the vehicle and a target position to be reached by the vehicle;

[0169] Step S2, constructing a topological structure diagram of the driving path based on the road condition parameter information, the starting position and the target position;

[0170] Step S3, searching the topological structure diagram using the target search model to obtain a driving path, wherein the constraints of the target search model include battery power consumption constraints and battery capacity constraints.

[0171] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0172] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0173] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0174] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0175] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0176] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0177] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0178] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A vehicle path planning method, characterized in that: include: In response to receiving a driving route generation instruction, obtaining vehicle position information and road condition parameter information, wherein the position information includes a starting position of the vehicle and a target position to be reached by the vehicle; Constructing a topological structure diagram of the driving path based on the road condition parameter information, the starting position and the target position; The topological structure diagram is searched using a target search model to obtain the driving path, wherein the constraint conditions of the target search model include a battery power consumption constraint condition and a battery capacity constraint condition.

2. The vehicle path planning method according to claim 1, characterized in that: Constructing the topological structure diagram of the driving path based on the road condition parameter information, the starting position and the target position includes: Based on the starting position and the target position, determining a selection range of the topological structure diagram; Determine the node information of the topological structure diagram based on the road unit information and the road length information in the road condition parameter information; Based on the target energy loss information and node information between road units in the road condition parameter information, the topological structure diagram is determined, wherein the topological structure diagram includes connection relationships and weight information between the nodes.

3. The vehicle path planning method according to claim 2, characterized in that: Determining the weight information of the topological structure graph includes: Acquiring height information of the road unit, distance information between the road units, and vehicle travel speed information; Determining potential energy information between the road units based on the height information and the mass information of the vehicle; Determine the target energy loss information between the road units based on the potential energy information, the distance information and the vehicle travel speed information; The weight information is determined according to the target energy loss information.

4. The vehicle path planning method according to claim 3, characterized in that: Determining the target energy loss information between the road units based on the potential energy information, the distance information and the vehicle travel speed information includes: Determine third energy loss information based on the sum of the first energy loss information, the second energy loss information and the potential energy information, wherein the first energy loss information is determined based on the distance information, the mass information and the friction coefficient, and is used to represent the energy lost by the vehicle overcoming rolling resistance, and the second energy loss information is determined based on the distance information, the vehicle driving speed information and the air resistance coefficient, and is used to represent the energy lost by the vehicle overcoming air resistance; In response to the third energy loss information being greater than a preset threshold, determining the target energy loss information based on a first energy conversion factor of the vehicle and the third energy loss information; In response to the third energy loss information being less than or equal to the preset threshold, the target energy loss information is determined based on a second energy conversion factor of the vehicle and the third energy loss information.

5. The vehicle path planning method according to claim 1, characterized in that: The topological structure diagram is searched using the target search model to obtain the driving path including: Based on the estimated cost and the actual cost, constructing an energy loss objective function, wherein the estimated cost is determined based on the distance between the node to be visited and the target location, and the actual cost is determined based on the weights of the starting location and the node to be visited; Based on the initial power information and battery capacity information of the battery, construct a target constraint relationship set of the energy loss objective function; The target search model is used to search the topological structure diagram based on the target constraint relationship set and the energy loss objective function to obtain the driving path.

6. The vehicle path planning method according to claim 5, characterized in that: Based on the initial power information and battery capacity information of the battery, the target constraint relationship set for constructing the energy loss objective function includes: Constructing a battery power constraint condition of the driving path based on the weights in the topological structure diagram and the initial power information; Constructing a battery capacity constraint condition of the driving path based on the weight in the topological structure diagram, the difference between the initial power information and the battery capacity information; The target constraint relationship set is constructed based on the battery power constraint condition and the battery capacity constraint condition.

7. The vehicle path planning method according to claim 5, characterized in that: The target search model is used to search the topology diagram based on the target constraint relationship set and the energy loss objective function to obtain the driving path, which includes: Selecting the to-be-visited node from a first node list based on the energy loss objective function, wherein the first node list is determined based on node information in the topological structure diagram; In response to the to-be-visited node satisfying the target constraint relationship set, placing the to-be-visited node into a node set of the driving path; Remove the node to be visited from the first node list to obtain a second node list; A node to be visited is selected again from the second node list based on the energy loss objective function, and it is determined whether the node to be visited satisfies the target constraint relationship set until the node to be visited is a node corresponding to the target position.

8. A vehicle path planning device, characterized in that: include: An acquisition module, configured to acquire the vehicle's location information, battery information, and road condition parameter and road structure information in response to receiving a driving route generation instruction, wherein the battery location information includes the vehicle's starting location and the vehicle's target location; A construction module, used to construct a topological structure diagram of the driving path based on the road condition parameter information, the starting position and the target position; The determination module is used to search the topological structure diagram using a target search model to obtain the driving path, wherein the constraints of the target search model include battery power consumption constraints and battery capacity constraints.

9. An electronic device, characterized in that: include: A memory storing an executable program; A processor is used to run the program, wherein the program executes the vehicle path planning method described in any one of claims 1 to 7 when running.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the storage medium is located is controlled to execute the vehicle path planning method according to any one of claims 1 to 7.

11. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the vehicle path planning method according to any one of claims 1 to 7.

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