Electric vehicle path optimization method and device based on carbon emission, equipment and medium

Abstract

The invention relates to the technical field of path optimization and the technical field of energy management, and discloses an electric vehicle path optimization method and device based on carbon emission, equipment and a medium, and the method comprises the steps: obtaining the current position, a target charging station and the remaining endurance mileage of an electric vehicle, and obtaining the remaining driving mileage from the current position to the target charging station; when the residual driving mileage is greater than the residual endurance mileage, obtaining each path from the current position to the target charging station; adding the total energy consumption cost of each path and the charging cost of the transfer charging station corresponding to each path to obtain the cost of each path; and generating the total cost corresponding to each path according to the carbon emission cost of each path, the time cost of each path and the expense cost of each path, selecting the path with the minimum total cost as the low-carbon path of the electric vehicle, and updating the optimal path of the electric vehicle into the low-carbon path. The travel efficiency of the electric vehicle is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of route optimization and energy management, and in particular to a method, device, equipment and medium for optimizing the route of an electric vehicle based on carbon emissions. Background Art

[0002] As global climate change intensifies and carbon emissions become more prominent, electric vehicles, as a clean energy means of transportation, play an important role in reducing carbon emissions and achieving green transportation. Compared with traditional fuel vehicles, electric vehicles do not directly produce carbon emissions during driving, which can effectively reduce greenhouse gas emissions and help reduce carbon emissions.

[0003] However, the widespread use of electric vehicles has also brought new challenges to the power grid, especially during periods of concentrated charging demand, when the load pressure on the power grid increases significantly, which can easily lead to problems such as unstable power grid operation. At present, there are some path optimization methods based on traffic flow, but these methods ignore the factor of carbon emissions. Therefore, how to optimize the path of electric vehicles based on carbon emissions is an urgent problem to be solved. Summary of the invention

[0004] The present invention provides a method, device, computer equipment and storage medium for optimizing the path of an electric vehicle based on carbon emissions, so as to solve the technical problem that how to optimize the path of an electric vehicle based on carbon emissions is an urgent problem to be solved.

[0005] In a first aspect, a method for optimizing an electric vehicle route based on carbon emissions is provided, comprising:

[0006] Obtain the current location, target charging station and remaining driving range of the electric vehicle, and obtain the remaining driving range from the current location to the target charging station;

[0007] When the remaining driving mileage is greater than the remaining cruising range, obtaining each path from the current position to the target charging station;

[0008] Generate the carbon emission cost of each of the paths according to the carbon emission coefficient of each road section in each of the paths, the length of each road section and a preset carbon emission cost generation model;

[0009] Save the carbon emission cost of each path, obtain the travel time of each section in each path, add up the travel time of each section to obtain the time cost of each path, obtain the energy consumption cost of each section in each path, add up the energy consumption cost of each section to obtain the total energy consumption cost of each path;

[0010] The total energy consumption cost of each path and the charging cost of the transfer charging station corresponding to each path are added together to obtain the cost of each path;

[0011] According to the carbon emission cost of each path, the time cost of each path, the expense cost of each path and a preset total cost generation model, the total cost corresponding to each path is generated, the path with the smallest total cost is selected as the low-carbon path of the electric vehicle, and the optimal path of the electric vehicle is updated to the low-carbon path.

[0012] Wherein, the carbon emission cost generation model is:

[0013]

[0014] Among them, C carbon (v i ) is the carbon emission cost of path vi, path vi represents the i-th path, ek is the carbon emission coefficient of the k-th section in path vi, d k is the length of the kth segment in path vi, n is the total number of segments, represents the carbon emissions of path vi, β carbon It is the cost coefficient of carbon emissions, which indicates the cost per unit of carbon emissions.

[0015] Wherein, the total cost generation model is:

[0016] TotalCost(v i )=α1×C carbon (v i )+α2×T cost (v i )+α3×C cost (v i );

[0017] Among them, TotalCost(v i ) is the total cost of path vi, α1 is the first weight coefficient, α2 is the second weight coefficient, α3 is the third weight coefficient, C carbon (v i ) is the carbon emission cost of path vi, T cost (v i ) is the time cost of path vi, C cost (v i ) is the cost of path vi, and the sum of α1, α2, and α3 is 1.

[0018] Further, when the remaining driving mileage is greater than the remaining cruising range, obtaining each path from the current position to the target charging station includes:

[0019] When the remaining driving mileage is greater than the remaining cruising range, obtaining a path planning result from the current location to the target charging station;

[0020] In the path planning result, each path from the current position to the target charging station is obtained.

[0021] Furthermore, the carbon emission cost of each path is generated according to the carbon emission coefficient of each road section in each path, the length of each road section and a preset carbon emission cost generation model, including:

[0022] Accessing a data source, and obtaining from the data source the carbon emission coefficient of each section in each of the paths, and obtaining the length of each section in each of the paths;

[0023] The carbon emission cost of each path is generated according to the carbon emission coefficient of each road section in each path, the length of each road section and a preset carbon emission cost generation model.

[0024] Furthermore, the carbon emission cost of each of the paths is saved, the travel time of each section in each of the paths is obtained, the travel time of each section is added up to obtain the time cost of each of the paths, the energy consumption cost of each section in each of the paths is obtained, the energy consumption cost of each section is added up to obtain the total energy consumption cost of each of the paths, including:

[0025] Obtaining a saving instruction, executing the saving instruction, and saving the carbon emission cost of each of the paths;

[0026] Obtain the travel time of each section in each of the paths, add up the travel time of each section to obtain the time cost of each path, obtain the energy consumption cost of each section in each of the paths, add up the energy consumption cost of each section to obtain the total energy consumption cost of each path.

[0027] Furthermore, the total energy consumption cost of each path and the charging cost of the transfer charging station corresponding to each path are added together to obtain the cost of each path, including:

[0028] Obtaining cost information, and obtaining the charging cost of the transfer charging station corresponding to each of the paths from the cost information;

[0029] The total energy consumption cost of each path and the charging cost of the transfer charging station corresponding to each path are added together to obtain the cost of each path.

[0030] Furthermore, the total cost corresponding to each path is generated according to the carbon emission cost of each path, the time cost of each path, the expense cost of each path and a preset total cost generation model, the path with the smallest total cost is selected as the low-carbon path of the electric vehicle, and the optimal path of the electric vehicle is updated to the low-carbon path, including:

[0031] Generate the total cost corresponding to each of the paths according to the carbon emission cost of each of the paths, the time cost of each of the paths, the expense cost of each of the paths, and a preset total cost generation model;

[0032] The total cost corresponding to each of the paths is sorted in order from small to large, the path with the smallest total cost is selected as the low-carbon path of the electric vehicle, and the optimal path of the electric vehicle is updated to the low-carbon path.

[0033] Furthermore, after generating the total cost corresponding to each path according to the carbon emission cost of each path, the time cost of each path, the expense cost of each path and the preset total cost generation model, selecting the path with the smallest total cost as the low-carbon path of the electric vehicle, and updating the optimal path of the electric vehicle to the low-carbon path, the electric vehicle path optimization method includes:

[0034] A display page is obtained, and a low-carbon path of the electric vehicle is displayed through the display page.

[0035] In a second aspect, a carbon emission-based electric vehicle route optimization device is provided, comprising:

[0036] A first acquisition module is used to acquire the current position, target charging station and remaining driving range of the electric vehicle, and acquire the remaining driving range from the current position to the target charging station;

[0037] A second acquisition module, configured to acquire each path from the current position to the target charging station when the remaining driving mileage is greater than the remaining cruising range;

[0038] A generation module, used to generate the carbon emission cost of each of the paths according to the carbon emission coefficient of each section in each of the paths, the length of each section and a preset carbon emission cost generation model;

[0039] The third acquisition module is used to save the carbon emission cost of each path, obtain the travel time of each section in each path, add the travel time of each section to obtain the time cost of each path, obtain the energy consumption cost of each section in each path, add the energy consumption cost of each section to obtain the total energy consumption cost of each path;

[0040] An adding module, used for adding the total energy consumption cost of each path and the charging cost of the transfer charging station corresponding to each path, to obtain the cost of each path;

[0041] The optimization module is used to generate the total cost corresponding to each path according to the carbon emission cost of each path, the time cost of each path, the expense cost of each path and a preset total cost generation model, select the path with the smallest total cost as the low-carbon path of the electric vehicle, and update the optimal path of the electric vehicle to the low-carbon path.

[0042] In a third aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned electric vehicle path optimization method when executing the computer program.

[0043] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned electric vehicle path optimization method are implemented.

[0044] The present application provides a method, device, computer equipment and storage medium for optimizing electric vehicle routes based on carbon emissions, which obtain the current position, target charging station and remaining cruising range of the electric vehicle, and obtain the remaining driving range from the current position to the target charging station; when the remaining driving range is greater than the remaining cruising range, obtain each path from the current position to the target charging station; generate the carbon emission cost of each path according to the carbon emission coefficient of each section in each path, the length of each section and a preset carbon emission cost generation model; save the carbon emission cost of each path, obtain the travel time of each section in each path, add up the travel time of each section to obtain the time cost of each path, obtain the energy consumption cost of each section in each path, add up the energy consumption cost of each section to obtain the total energy consumption cost of each path; add up the total energy consumption cost of each path and the charging cost of the transfer charging station corresponding to each path to obtain the cost cost of each path; and calculate the carbon emission cost of each path according to the carbon emission cost of each path, the time cost of each path, and the time cost of each path. The cost of the path and the preset total cost generation model are used to generate the total cost corresponding to each path, the path with the smallest total cost is selected as the low-carbon path of the electric vehicle, and the optimal path of the electric vehicle is updated to the low-carbon path. The beneficial effects are in two aspects. On the one hand, according to the carbon emission cost of each path, the time cost of each path, the cost cost of each path and the preset total cost generation model, the total cost corresponding to each path is generated, the path with the smallest total cost is selected as the low-carbon path of the electric vehicle, and the optimal path of the electric vehicle is updated to the low-carbon path. Since the low-carbon path is determined based on the carbon emission cost, time cost and cost cost, the carbon emission factor is not ignored. Therefore, when the electric vehicle is charged on the low-carbon path, it can disperse the load of the power grid and avoid excessive impact on the power grid during peak hours of electricity consumption, which is conducive to maintaining the stable operation of the power grid and reducing the risk of power grid failure caused by excessive load; on the other hand, since the total cost generation model will not be affected by human intervention, it is conducive to improving the reliability of the obtained low-carbon path. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0046] Figure 1 is a schematic diagram of an application environment of an electric vehicle path optimization method in one embodiment of the present invention;

[0047] Figure 2 A schematic diagram of a flow chart of an electric vehicle path optimization method provided by an embodiment of the present invention;

[0048] Figure 3 yes Figure 2 A schematic flow chart of a specific implementation of step S23;

[0049] Figure 4 yes Figure 2 A schematic flow chart of a specific implementation of step S25;

[0050] Figure 5 yes Figure 2 A schematic flow chart of a specific implementation of step S26;

[0051] Figure 6 is a structural schematic diagram of an electric vehicle path optimization device in one embodiment of the present invention;

[0052] Figure 7 is a schematic diagram of a structure of a computer device in one embodiment of the present invention;

[0053] Figure 8 FIG. 4 is a schematic diagram of the distribution of charging stations provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are 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 are within the scope of protection of the present invention.

[0055] See also Figure 1 , Figure 1 FIG. 1 is a schematic diagram of an application environment of an electric vehicle path optimization method according to an embodiment of the present invention. The electric vehicle path optimization method provided by the embodiment of the present invention can be applied in the following embodiments: Figure 1 In an application environment, a client communicates with a server through a network.

[0056] The server obtains the current location, target charging station and remaining driving range of the electric vehicle through the client, and obtains the remaining driving range from the current location to the target charging station;

[0057] When the remaining driving mileage is greater than the remaining cruising range, obtaining each path from the current position to the target charging station;

[0058] Generate the carbon emission cost of each of the paths according to the carbon emission coefficient of each road section in each of the paths, the length of each road section and a preset carbon emission cost generation model;

[0059] Save the carbon emission cost of each path, obtain the travel time of each section in each path, add up the travel time of each section to obtain the time cost of each path, obtain the energy consumption cost of each section in each path, add up the energy consumption cost of each section to obtain the total energy consumption cost of each path;

[0060] The total energy consumption cost of each path and the charging cost of the transfer charging station corresponding to each path are added together to obtain the cost of each path;

[0061] According to the carbon emission cost of each path, the time cost of each path, the expense cost of each path and a preset total cost generation model, the total cost corresponding to each path is generated, the path with the smallest total cost is selected as the low-carbon path of the electric vehicle, and the optimal path of the electric vehicle is updated to the low-carbon path.

[0062] In the scheme implemented by the above-mentioned electric vehicle path optimization method, device, equipment and medium, the beneficial effects are in two aspects. On the one hand, according to the carbon emission cost of each path, the time cost of each path, the expense cost of each path and the preset total cost generation model, the total cost corresponding to each path is generated, and the path with the smallest total cost is selected as the low-carbon path of the electric vehicle, and the optimal path of the electric vehicle is updated to the low-carbon path. Since the low-carbon path is determined based on the carbon emission cost, time cost and expense cost, the carbon emission factor is not ignored. Therefore, when the electric vehicle is charging on the low-carbon path, it can disperse the load of the power grid and avoid excessive impact on the power grid during peak power consumption hours, which is conducive to maintaining the stable operation of the power grid and reducing the risk of power grid failure caused by excessive load; on the other hand, since the total cost generation model will not be affected by human intervention, it is conducive to improving the reliability of the obtained low-carbon path.

[0063] The device running the client is referred to as a client device.

[0064] The device running the server is referred to as the server device.

[0065] Among them, client devices include but are not limited to Internet of Vehicles terminals, smart phones, personal computers, tablets and portable wearable devices.

[0066] The server device can be implemented by an independent server or a server cluster composed of multiple servers. The present invention is described in detail below through specific embodiments.

[0067] See also Figure 2 , Figure 2 A flow chart of an electric vehicle path optimization method provided by an embodiment of the present invention includes the following steps:

[0068] S21, obtaining the current position, target charging station and remaining driving range of the electric vehicle, and obtaining the remaining driving range from the current position to the target charging station;

[0069] Among them, the target charging station refers to the charging facility located at the target location.

[0070] Among them, the target charging stations not only improve the endurance of electric vehicles, but also promote the popularization of electric vehicles and promote the development of green transportation.

[0071] S22, when the remaining driving mileage is greater than the remaining cruising range, obtaining each path from the current position to the target charging station;

[0072] Wherein, when the remaining driving mileage is greater than the remaining cruising range, obtaining each path from the current location to the target charging station includes:

[0073] When the remaining driving mileage is greater than the remaining cruising range, obtaining a path planning result from the current location to the target charging station;

[0074] In the path planning result, each path from the current position to the target charging station is obtained.

[0075] S23, generating a carbon emission cost for each of the paths according to the carbon emission coefficient of each road section in each of the paths, the length of each road section, and a preset carbon emission cost generation model;

[0076] Wherein, the carbon emission cost generation model is:

[0077]

[0078] Among them, C carbon (v i ) is the carbon emission cost of path vi, path vi represents the i-th path, ek is the carbon emission coefficient of the k-th section in path vi, d k is the length of the kth segment in path vi, n is the total number of segments, represents the carbon emissions of path vi, β carbonIt is the cost coefficient of carbon emissions, which indicates the cost per unit of carbon emissions.

[0079] Among them, carbon emissions refer to the total amount of carbon dioxide released into the atmosphere. It is an important indicator to measure the impact of environmental pollution and climate change, and is highly logical and scientific. In various fields such as production, transportation, and energy consumption, the combustion of fossil fuels, industrial production processes, and land use changes will generate a large amount of carbon dioxide emissions.

[0080] Among them, the carbon emissions of path vi mainly refer to the carbon emissions of the transfer charging station corresponding to path vi.

[0081] Among them, the cost corresponding to each unit of carbon emissions refers to the economic cost required to reduce or offset one ton of carbon dioxide emissions.

[0082] S24, saving the carbon emission cost of each of the paths, obtaining the travel time of each section in each of the paths, adding up the travel time of each section to obtain the time cost of each of the paths, obtaining the energy consumption cost of each section in each of the paths, adding up the energy consumption cost of each section to obtain the total energy consumption cost of each of the paths;

[0083] The carbon emission cost of each path is saved, the travel time of each section in each path is obtained, the travel time of each section is added up to obtain the time cost of each path, the energy consumption cost of each section in each path is obtained, the energy consumption cost of each section is added up to obtain the total energy consumption cost of each path, including:

[0084] Obtaining a saving instruction, executing the saving instruction, and saving the carbon emission cost of each of the paths;

[0085] Obtain the travel time of each section in each of the paths, add up the travel time of each section to obtain the time cost of each path, obtain the energy consumption cost of each section in each of the paths, add up the energy consumption cost of each section to obtain the total energy consumption cost of each path.

[0086] Among them, the total energy consumption cost of each path can help the total cost generation model to make more economical and reasonable travel choices, thereby saving the transportation expenses of electric vehicles and improving the travel efficiency of electric vehicles.

[0087] S25, adding the total energy consumption cost of each path and the charging cost of the transfer charging station corresponding to each path to obtain the cost of each path;

[0088] See also Figure 8 , Figure 8FIG. 4 is a schematic diagram of the distribution of charging stations provided in one embodiment of the present invention.

[0089] Figure 8 Including multiple charging stations, which are carefully planned and laid out to provide users with convenient and fast charging services, ensuring that electric vehicles can replenish power in time during driving to meet their needs for long-distance travel or daily commuting.

[0090] The plurality of charging stations include charging station 1, charging station 2, charging station 3, charging station 4, charging station 5, charging station 6, charging station 7, charging station 8, charging station 9, charging station 10, charging station 11, charging station 11, charging station 12, charging station 13, charging station 14, charging station 15, charging station 16, charging station 17, charging station 18, charging station 19, and charging station 20.

[0091] The target charging station can be any one of charging station 1, charging station 2, charging station 3, charging station 4, charging station 5, charging station 6, charging station 7, charging station 8, charging station 9, charging station 10, charging station 11, charging station 11, charging station 12, charging station 13, charging station 14, charging station 15, charging station 16, charging station 17, charging station 18, charging station 19, and charging station 20.

[0092] For the sake of illustration, an example is given below:

[0093] For example, the target charging station is charging station 20, the current location of the electric vehicle is charging station 10, and the electric vehicle needs to go from charging station 10 to charging station 20. The charging stations between charging station 10 and charging station 20 are all transit charging stations.

[0094] For example, the target charging station is charging station 19, and the current location of the electric vehicle is charging station 15. The electric vehicle needs to go from charging station 15 to charging station 19. The charging stations between charging station 15 and charging station 29 are all transit charging stations.

[0095] Among them, the transfer charging station can effectively alleviate the range anxiety of electric vehicle users and lay a solid foundation for the widespread use of electric vehicles.

[0096] S26, generating the total cost corresponding to each path according to the carbon emission cost of each path, the time cost of each path, the expense cost of each path and a preset total cost generation model, selecting the path with the smallest total cost as the low-carbon path of the electric vehicle, and updating the optimal path of the electric vehicle to the low-carbon path.

[0097] Wherein, the total cost generation model is:

[0098] TotalCost(v i)=α1×C carbon (v i )+α2×T cost (v i )+α3×C cost (v i );

[0099] Among them, TotalCost(v i ) is the total cost of path vi, α1 is the first weight coefficient, α2 is the second weight coefficient, α3 is the third weight coefficient, C carbon (v i ) is the carbon emission cost of path vi, T cost (v i ) is the time cost of path vi, C cost (v i ) is the cost of path vi, and the sum of α1, α2, and α3 is 1.

[0100] Among them, when the remaining driving mileage is greater than the remaining cruising range, the total cost generation model is used to optimize the path, and the three factors of carbon emission cost, time cost and expense cost in path planning are taken into consideration at the same time to generate a low-carbon path for the electric vehicle. The path with the smallest total cost is selected as the low-carbon path of the electric vehicle, and the optimal path of the electric vehicle is updated to the low-carbon path, which can ensure that the electric vehicle reduces carbon emission cost, time cost and expense cost during driving.

[0101] Among them, the low-carbon path refers to the path of reducing carbon emissions.

[0102] Among them, the low-carbon path usually means that the power supply of the transfer charging station of the low-carbon path relies more on renewable energy, such as solar energy, wind energy, etc., rather than fossil fuels. Therefore, charging at the transfer charging stations of these low-carbon paths can reduce carbon emissions caused by burning fossil fuels, thereby helping to alleviate the environmental pressure on the power grid caused by carbon emissions.

[0103] Among them, there are three schemes for the settings of α1, α2, and α3, which are described in detail as follows:

[0104] Option 1: α1=0.3, α2=0.3, α3=0.4.

[0105] Solution 2: α1=0.4, α2=0.3, α3=0.3;

[0106] Option three, α1=0.3, α2=0.4, α3=0.3.

[0107] By comparing the carbon emissions, driving time and cost of these three solutions, the path optimization effect of the present invention under different optimization weight configurations can be more comprehensively evaluated.

[0108] Among them, Plan 1, Plan 2, and Plan 3 can be executed in the order of Plan 1, Plan 2, and Plan 3, or in the order of Plan 1, Plan 3, and Plan 2, without any limitation here.

[0109] Wherein, after generating the total cost corresponding to each of the paths according to the carbon emission cost of each of the paths, the time cost of each of the paths, the expense cost of each of the paths and the preset total cost generation model, selecting the path with the smallest total cost as the low-carbon path of the electric vehicle, and updating the optimal path of the electric vehicle to the low-carbon path, the electric vehicle path optimization method comprises:

[0110] A display page is obtained, and a low-carbon path of the electric vehicle is displayed through the display page.

[0111] Among them, by displaying the low-carbon path of the electric vehicle, the electric vehicle can be guided to charge at the transfer charging station of the low-carbon path, because the power supply of the transfer charging station of the low-carbon path relies more on renewable energy, such as solar energy, wind energy, etc., rather than fossil fuels. Therefore, charging at the transfer charging stations of these low-carbon paths can reduce carbon emissions caused by burning fossil fuels, thereby helping to alleviate the environmental pressure on the power grid caused by carbon emissions.

[0112] In the embodiment of the present invention, the beneficial effects are in two aspects. On the one hand, according to the carbon emission cost of each path, the time cost of each path, the expense cost of each path and the preset total cost generation model, the total cost corresponding to each path is generated, and the path with the smallest total cost is selected as the low-carbon path of the electric vehicle, and the optimal path of the electric vehicle is updated to the low-carbon path. Since the low-carbon path is determined based on the carbon emission cost, time cost and expense cost, the factor of carbon emission is not ignored. Therefore, when the electric vehicle is charging on the low-carbon path, it can disperse the load of the power grid and avoid excessive impact on the power grid during peak power consumption hours, which is beneficial to maintaining the stable operation of the power grid and reducing the risk of power grid failure caused by excessive load. On the other hand, since the total cost generation model will not be affected by human intervention, it is beneficial to improve the reliability of the obtained low-carbon path.

[0113] See also Figure 3 , Figure 3 yes Figure 2 A specific implementation flow diagram of step S23 is described in detail as follows:

[0114] S31, accessing a data source, and obtaining from the data source the carbon emission coefficient of each section in each of the paths, and obtaining the length of each section in each of the paths;

[0115] S32, generating the carbon emission cost of each of the paths according to the carbon emission coefficient of each road section in each of the paths, the length of each road section and a preset carbon emission cost generation model.

[0116] In an embodiment of the present invention, the carbon emission cost of each path is generated according to the carbon emission coefficient of each section in each path, the length of each section and a preset carbon emission cost generation model. In this way, path planning can be performed based on the carbon emission cost of each path to ensure that electric vehicles reduce carbon emissions during driving.

[0117] See also Figure 4 , Figure 4 yes Figure 2 A specific implementation flow diagram of step S25 is described in detail as follows:

[0118] S41, obtaining cost information, and obtaining the charging cost of the transfer charging station corresponding to each of the paths from the cost information;

[0119] S42, adding the total energy consumption cost of each path and the charging cost of the transfer charging station corresponding to each path to obtain the cost of each path.

[0120] In an embodiment of the present invention, the total energy consumption cost of each path and the charging cost of the transfer charging station corresponding to each path are added to obtain the cost of each path. In this way, path planning can be carried out according to the cost of each path to ensure that the electric vehicle reduces the cost during driving.

[0121] See also Figure 5 , Figure 5 yes Figure 2 A specific implementation flow diagram of step S26 is described in detail as follows:

[0122] S51, generating a total cost corresponding to each of the paths according to the carbon emission cost of each of the paths, the time cost of each of the paths, the expense cost of each of the paths, and a preset total cost generation model;

[0123] S52, sorting the total costs corresponding to each of the paths in ascending order, selecting the path with the smallest total cost as the low-carbon path of the electric vehicle, and updating the optimal path of the electric vehicle to the low-carbon path.

[0124] In an embodiment of the present invention, the total cost corresponding to each of the paths is sorted in ascending order, the path with the smallest total cost is selected as the low-carbon path of the electric vehicle, and the optimal path of the electric vehicle is updated to the low-carbon path. Since the low-carbon path is determined based on carbon emission cost, time cost, and expense cost, the factor of carbon emissions is not ignored. Therefore, when the electric vehicle is charging on the low-carbon path, it can disperse the load of the power grid and avoid excessive impact on the power grid during peak hours of electricity consumption, which is conducive to maintaining the stable operation of the power grid and reducing the risk of power grid failure due to excessive load.

[0125] See also Figure 6 , Figure 6 : is a schematic diagram of a structure of an electric vehicle path optimization device in one embodiment of the present invention, Figure 6 As shown, the electric vehicle path optimization device includes a first acquisition module 101, a second acquisition module 102, a generation module 103, a third acquisition module 104, an addition module 105, and an optimization module 106. The functional modules are described in detail as follows:

[0126] The first acquisition module 101 is used to acquire the current position, target charging station and remaining driving range of the electric vehicle, and acquire the remaining driving range from the current position to the target charging station;

[0127] A second acquisition module 102, configured to acquire each path from the current position to the target charging station when the remaining driving mileage is greater than the remaining cruising range;

[0128] A generation module 103, configured to generate the carbon emission cost of each of the paths according to the carbon emission coefficient of each of the paths, the length of each of the paths and a preset carbon emission cost generation model;

[0129] The third acquisition module 104 is used to save the carbon emission cost of each path, obtain the travel time of each section in each path, add the travel time of each section to obtain the time cost of each path, obtain the energy consumption cost of each section in each path, add the energy consumption cost of each section to obtain the total energy consumption cost of each path;

[0130] An adding module 105, configured to add the total energy consumption cost of each path and the charging cost of the transfer charging station corresponding to each path to obtain the cost of each path;

[0131] The optimization module 106 is used to generate the total cost corresponding to each path according to the carbon emission cost of each path, the time cost of each path, the expense cost of each path and a preset total cost generation model, select the path with the smallest total cost as the low-carbon path of the electric vehicle, and update the optimal path of the electric vehicle to the low-carbon path.

[0132] In the embodiment of the present invention, the beneficial effects are in two aspects. On the one hand, according to the carbon emission cost of each path, the time cost of each path, the expense cost of each path and the preset total cost generation model, the total cost corresponding to each path is generated, and the path with the smallest total cost is selected as the low-carbon path of the electric vehicle, and the optimal path of the electric vehicle is updated to the low-carbon path. Since the low-carbon path is determined based on the carbon emission cost, time cost and expense cost, the factor of carbon emission is not ignored. Therefore, when the electric vehicle is charging on the low-carbon path, it can disperse the load of the power grid and avoid excessive impact on the power grid during peak power consumption hours, which is beneficial to maintaining the stable operation of the power grid and reducing the risk of power grid failure caused by excessive load. On the other hand, since the total cost generation model will not be affected by human intervention, it is beneficial to improve the reliability of the obtained low-carbon path.

[0133] The specific limitations on the electric vehicle path optimization device can be found in the above limitations on the electric vehicle path optimization method, which will not be repeated here.

[0134] Each module in the above-mentioned electric vehicle path optimization device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0135] See also Figure 7 , Figure 7 1 is another structural diagram of a computer device in one embodiment of the present invention. In one embodiment, a computer device is provided. The computer device is a server device or a client device. The internal structure diagram thereof can be as shown in FIG. Figure 7As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external database. When the computer program is executed by the processor, the functions or steps of a method for optimizing the path of an electric vehicle based on carbon emissions can be realized.

[0136] In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor.

[0137] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can refer to the relevant description of the aforementioned method embodiment. In order to avoid repetition, they will not be described one by one here.

[0138] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a graphics processing unit (GPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0139] The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent only possible variations. Unless explicitly required, separate components and functions are optional, and the order of operations may vary. Parts and sub-samples of some embodiments may be included in or replace parts and sub-samples of other embodiments. Moreover, the terms used in this application are only used to describe the embodiments and are not used to limit the claims.

[0140] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. Technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of the present disclosure. Technicians can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0141] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A method for optimizing electric vehicle routes based on carbon emissions, characterized in that: include: Obtain the current location, target charging station and remaining driving range of the electric vehicle, and obtain the remaining driving range from the current location to the target charging station; When the remaining driving mileage is greater than the remaining cruising range, obtaining each path from the current position to the target charging station; Generate the carbon emission cost of each of the paths according to the carbon emission coefficient of each road section in each of the paths, the length of each road section and a preset carbon emission cost generation model; Save the carbon emission cost of each path, obtain the travel time of each section in each path, add up the travel time of each section to obtain the time cost of each path, obtain the energy consumption cost of each section in each path, add up the energy consumption cost of each section to obtain the total energy consumption cost of each path; The total energy consumption cost of each path and the charging cost of the transfer charging station corresponding to each path are added together to obtain the cost of each path; According to the carbon emission cost of each path, the time cost of each path, the expense cost of each path and a preset total cost generation model, the total cost corresponding to each path is generated, the path with the smallest total cost is selected as the low-carbon path of the electric vehicle, and the optimal path of the electric vehicle is updated to the low-carbon path.

2. The electric vehicle path optimization method according to claim 1, characterized in that: When the remaining driving mileage is greater than the remaining cruising range, obtaining each path from the current position to the target charging station includes: When the remaining driving mileage is greater than the remaining cruising range, obtaining a path planning result from the current location to the target charging station; In the path planning result, each path from the current position to the target charging station is obtained.

3. The electric vehicle path optimization method according to claim 1, characterized in that: The step of generating the carbon emission cost of each path according to the carbon emission coefficient of each road section in each path, the length of each road section and a preset carbon emission cost generation model includes: Accessing a data source, and obtaining from the data source the carbon emission coefficient of each section in each of the paths, and obtaining the length of each section in each of the paths; The carbon emission cost of each path is generated according to the carbon emission coefficient of each road section in each path, the length of each road section and a preset carbon emission cost generation model.

4. The electric vehicle path optimization method according to claim 1, characterized in that: The carbon emission cost of each path is saved, the travel time of each section in each path is obtained, the travel time of each section is added up to obtain the time cost of each path, the energy consumption cost of each section in each path is obtained, the energy consumption cost of each section is added up to obtain the total energy consumption cost of each path, including: Obtaining a saving instruction, executing the saving instruction, and saving the carbon emission cost of each of the paths; Obtain the travel time of each section in each of the paths, add up the travel time of each section to obtain the time cost of each path, obtain the energy consumption cost of each section in each of the paths, add up the energy consumption cost of each section to obtain the total energy consumption cost of each path.

5. The electric vehicle path optimization method according to claim 1, characterized in that: The total energy consumption cost of each path and the charging cost of the transfer charging station corresponding to each path are added together to obtain the cost of each path, including: Obtaining cost information, and obtaining the charging cost of the transfer charging station corresponding to each of the paths from the cost information; The total energy consumption cost of each path and the charging cost of the transfer charging station corresponding to each path are added together to obtain the cost of each path.

6. The electric vehicle path optimization method according to claim 1, characterized in that: The method generates the total cost corresponding to each path according to the carbon emission cost of each path, the time cost of each path, the expense cost of each path and a preset total cost generation model, selects the path with the smallest total cost as the low-carbon path of the electric vehicle, and updates the optimal path of the electric vehicle to the low-carbon path, including: Generate the total cost corresponding to each of the paths according to the carbon emission cost of each of the paths, the time cost of each of the paths, the expense cost of each of the paths, and a preset total cost generation model; The total cost corresponding to each of the paths is sorted in order from small to large, the path with the smallest total cost is selected as the low-carbon path of the electric vehicle, and the optimal path of the electric vehicle is updated to the low-carbon path.

7. The electric vehicle path optimization method according to claim 1, characterized in that: After generating the total cost corresponding to each path according to the carbon emission cost of each path, the time cost of each path, the expense cost of each path and the preset total cost generation model, selecting the path with the smallest total cost as the low-carbon path of the electric vehicle, and updating the optimal path of the electric vehicle to the low-carbon path, the electric vehicle path optimization method includes: A display page is obtained, and a low-carbon path of the electric vehicle is displayed through the display page.

8. An electric vehicle path optimization device based on carbon emissions, characterized in that: include: A first acquisition module is used to acquire the current position, target charging station and remaining driving range of the electric vehicle, and acquire the remaining driving range from the current position to the target charging station; A second acquisition module, configured to acquire each path from the current position to the target charging station when the remaining driving mileage is greater than the remaining cruising range; A generation module, used to generate the carbon emission cost of each of the paths according to the carbon emission coefficient of each section in each of the paths, the length of each section and a preset carbon emission cost generation model; The third acquisition module is used to save the carbon emission cost of each path, obtain the travel time of each section in each path, add the travel time of each section to obtain the time cost of each path, obtain the energy consumption cost of each section in each path, add the energy consumption cost of each section to obtain the total energy consumption cost of each path; An adding module, used for adding the total energy consumption cost of each path and the charging cost of the transfer charging station corresponding to each path, to obtain the cost of each path; The optimization module is used to generate the total cost corresponding to each path according to the carbon emission cost of each path, the time cost of each path, the expense cost of each path and a preset total cost generation model, select the path with the smallest total cost as the low-carbon path of the electric vehicle, and update the optimal path of the electric vehicle to the low-carbon path.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the electric vehicle path optimization method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the electric vehicle path optimization method according to any one of claims 1 to 7 are implemented.

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