CN116029450A - Vehicle charging planning method, device, server and storage medium - Google Patents
Vehicle charging planning method, device, server and storage medium Download PDFInfo
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Abstract
The application relates to a vehicle charging planning method, a vehicle charging planning device, a server and a storage medium, and the vehicle charging planning method is applied to the server, wherein the method comprises the following steps: receiving a destination and a charging planning request of a user; acquiring charging static data corresponding to a destination and charging dynamic data of a vehicle based on a charging planning request; and inputting the charging static data and the charging dynamic data into a pre-built charging planning model, and outputting an optimal charging planning strategy of the vehicle, wherein the pre-built charging planning model is obtained by a charging pile use model, a journey planning model, a vehicle working condition electricity consumption model and/or a user portrait model. Therefore, the technical problems that in the related art, the charging planning cannot be adjusted in real time according to the actual working condition of the vehicle and the dynamic working condition of the charging pile, the application of the actual vehicle environment parameters is lacking and the like are solved.
Description
Technical Field
The present disclosure relates to the field of charging planning technologies, and in particular, to a vehicle charging planning method, device, server, and storage medium.
Background
The problem of mileage anxiety exists in long-distance driving of a new energy automobile, the problem is often solved by planning a charging node in the driving process, however, the charging planning in the related art is stiff, real-time charging planning cannot be carried out according to the real working condition of the automobile, partial charging piles are caused to become hot spots by the charging planning, time-consuming queuing is needed when the automobile drives to the charging piles, meanwhile, real-time information synchronization is not needed, an automobile owner is inconvenient to make change of the charging planning in time, the charging planning is unreasonable, and the application of the environment parameters of the actual automobile is lacked to be improved.
Disclosure of Invention
The application provides a vehicle charging planning method, device, server and storage medium, which are used for solving the technical problems that in the related technology, charging planning cannot be adjusted in real time according to the actual working condition of a vehicle and the dynamic working condition of a charging pile, and the application of the actual vehicle environment parameters is lacking.
An embodiment of a first aspect of the present application provides a method for charging and planning a vehicle, applied to a server, where the method includes the following steps: receiving a destination and a charging planning request of a user; acquiring charging static data corresponding to the destination and charging dynamic data of the vehicle based on the charging planning request; and inputting the charging static data and the charging dynamic data into a pre-built charging planning model, and outputting an optimal charging planning strategy of the vehicle, wherein the pre-built charging planning model is obtained by a charging pile use model, a journey planning model, a vehicle working condition power consumption model and/or a user portrait model.
Optionally, in an embodiment of the present application, the acquiring, based on the charging plan request, charging static data corresponding to the destination and charging dynamic data of the vehicle includes: acquiring current position information of the vehicle, and planning a journey based on the current position information and the destination to generate at least one journey planning strategy; and acquiring corresponding charging static data based on each trip planning strategy.
Optionally, in an embodiment of the present application, the trip planning strategy includes a linear weight planning strategy, a route shortest planning strategy, and a time-consuming shortest planning strategy.
Optionally, in an embodiment of the present application, the inputting the charging static data and the charging dynamic data into a pre-built charging planning model outputs an optimal charging planning strategy of the vehicle, including: calculating the linear weight planning strategy, the route shortest planning strategy and the total route time consumption corresponding to the time consumption shortest planning strategy by using a pre-constructed charging planning model; and selecting a trip planning strategy with the shortest total time consumption of the trip, and generating the optimal charging planning strategy based on the trip planning strategy with the shortest total time consumption of the trip.
Optionally, in one embodiment of the present application, after outputting the optimal charging planning strategy of the vehicle, the method further includes: pushing the optimal charging planning strategy to a user; and controlling at least one acoustic reminding device and/or at least one optical reminding device of the vehicle to carry out charging reminding based on the optimal charging planning strategy.
An embodiment of a second aspect of the present application provides a charging planning device for a vehicle, applied to a server, where the device includes: the receiving module is used for receiving the destination and the charging planning request of the user; the acquisition module is used for acquiring the charging static data corresponding to the destination and the charging dynamic data of the vehicle based on the charging planning request; and the planning module is used for inputting the charging static data and the charging dynamic data into a pre-constructed charging planning model and outputting an optimal charging planning strategy of the vehicle, wherein the pre-constructed charging planning model is obtained by a charging pile use model, a journey planning model, a vehicle working condition power consumption model and/or a user portrait model.
Optionally, in one embodiment of the present application, the acquiring module includes: the first generation unit is used for acquiring current position information of the vehicle, planning a journey based on the current position information and the destination, and generating at least one journey planning strategy; and the acquisition unit is used for acquiring the corresponding charging static data based on each trip planning strategy.
Optionally, in an embodiment of the present application, the trip planning strategy includes a linear weight planning strategy, a route shortest planning strategy, and a time-consuming shortest planning strategy.
Optionally, in one embodiment of the present application, the planning module includes: the calculation unit is used for calculating the linear weight planning strategy, the route shortest planning strategy and the total route time consumption corresponding to the time consumption shortest planning strategy by utilizing a pre-constructed charging planning model; and the second generation unit is used for selecting a journey planning strategy with the shortest journey total time consumption and generating the optimal charging planning strategy based on the journey planning strategy with the shortest journey total time consumption.
Optionally, in one embodiment of the present application, further includes: the pushing module is used for pushing the optimal charging planning strategy to a user; and the control module is used for controlling at least one acoustic reminding device and/or at least one optical reminding device of the vehicle to carry out charging reminding based on the optimal charging planning strategy.
An embodiment of a third aspect of the present application provides a server, including: the vehicle charging planning system comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to realize the vehicle charging planning method according to the embodiment.
A fourth aspect of the present application provides a computer readable storage medium storing a computer program which when executed by a processor implements a method of charging a vehicle as above.
According to the method and the device for planning the charging of the vehicle, based on the destination of the user and the charging planning request, charging static data corresponding to the destination and charging dynamic data of the vehicle can be acquired pertinently, so that the rationality of charging planning is improved through fully utilizing the environment parameters of the actual vehicle, and the optimal charging planning strategy of the vehicle is obtained through inputting the charging static data and the charging dynamic data into a pre-built charging planning model, so that the charging planning is carried out according to the real working condition of the vehicle and the dynamic working condition of the charging pile, the planning result is more efficient, and the mileage anxiety problem of the user driving the new energy vehicle for a long distance can be effectively solved. Therefore, the technical problems that in the related art, the charging planning cannot be adjusted in real time according to the actual working condition of the vehicle and the dynamic working condition of the charging pile, the application of the actual vehicle environment parameters is lacking and the like are solved.
Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
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The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
fig. 1 is a flowchart of a method for planning charging of a vehicle according to an embodiment of the present application;
FIG. 2 is a schematic illustration of a method of charging planning for a vehicle according to one embodiment of the present application;
fig. 3 is a schematic structural diagram of a charging planning device for a vehicle according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a server according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and intended for the purpose of explaining the present application and are not to be construed as limiting the present application.
The following describes a charging planning method, device, server and storage medium for a vehicle according to an embodiment of the present application with reference to the accompanying drawings. Aiming at the technical problems that in the related technology mentioned in the background technology center, the charging planning cannot be adjusted in real time according to the actual working condition of the vehicle and the dynamic working condition of the charging pile, and the application of the actual working condition of the vehicle is lacking, and the like, the application provides a vehicle charging planning method. Therefore, the technical problems that in the related art, the charging planning cannot be adjusted in real time according to the actual working condition of the vehicle and the dynamic working condition of the charging pile, the application of the actual vehicle environment parameters is lacking and the like are solved.
Specifically, fig. 1 is a flow chart of a vehicle charging planning method according to an embodiment of the present application.
As shown in fig. 1, the vehicle charging planning method is applied to a server, wherein the method comprises the following steps:
in step S101, a destination of a user and a charge planning request are received.
In the actual execution process, the embodiment of the application can receive the destination and the charging planning request of the user, wherein the user can realize the input of the destination and the generation of the charging planning request through methods such as the setting of a touch control table of a vehicle, the setting of a mobile terminal and the like.
In step S102, based on the charge planning request, charge static data corresponding to the destination and charge dynamic data of the vehicle are acquired.
As a possible implementation manner, the embodiment of the application may obtain, based on a charging planning request of a user, charging static data corresponding to a destination by using, for example, a high-precision map, and obtain charging dynamic data by using tools such as a vehicle controller and a charging pile sharing platform.
The charging static data can include charging pile position information, national traffic data information, tourist attraction scenic spots, traffic service area information, different vehicle CLTC (China light vehicle driving condition duration) mileage information, real electricity consumption condition information of the vehicle under various conditions and the like, the charging static data are basic information about the vehicle service condition and are used as a blue book for subsequent charging planning, the charging static data also include vehicle habit information of a vehicle owner and comprise information of user driving habits such as electric doors, brakes, air conditioners, entertainment electricity consumption and the like, the information can be trained by aiming at users by using AI (Artificial Intelligence ) and big data, and user portraits are obtained and are used as important parameters of the user charging planning.
The charging dynamic data can comprise the service condition of a charging pile, the charging planning condition uploaded by other vehicles in the server, the real-time working condition in the running process of the vehicles, the real-time state of the vehicles and the like, and can be used as an important reference basis for real-time pushing charging planning.
Optionally, in an embodiment of the present application, based on the charging planning request, acquiring charging static data corresponding to the destination and charging dynamic data of the vehicle includes: acquiring current position information of a vehicle, and performing journey planning based on the current position information and a destination to generate at least one journey planning strategy; and acquiring corresponding charging static data based on each trip planning strategy.
In some embodiments, the embodiments of the present application may utilize tools such as a vehicle map, and perform trip planning based on current location information of a destination and a vehicle, so as to generate at least one trip planning policy, and further may obtain corresponding charging static data for each trip planning policy, so as to facilitate subsequent comparison of charging plans related to each trip planning policy, and obtain an optimal charging planning policy.
Optionally, in one embodiment of the present application, the trip planning strategy includes a linear weight planning strategy, a route shortest planning strategy, and a time-consuming shortest planning strategy.
In an actual implementation process, the trip planning strategy generated in the embodiment of the present application may include a linear weight planning strategy, a route shortest planning strategy and a time-consuming shortest planning strategy, where the linear weight planning strategy may perform route planning for a start point and an end point, multiple routes from the start point to the end point form a topology map, a traffic hub required to pass from the start point to the end point is a node in the topology map, and the number of charging piles between any two nodes is the weight of a line between the two nodes.
In step S103, the charging static data and the charging dynamic data are input into a pre-built charging planning model, and an optimal charging planning strategy of the vehicle is output, wherein the pre-built charging planning model is obtained by a charging pile usage model, a journey planning model, a vehicle working condition electricity consumption model and/or a user portrait model.
As a possible implementation manner, the embodiment of the application may pre-construct a charging planning model, where the pre-constructed charging planning model is obtained by a charging pile usage model, a journey planning model, a vehicle working condition electricity consumption model and/or a user portrait model, specifically, the embodiment of the application may filter, clean, bin, layer, model and train the obtained charging static data to obtain a charging pile usage model, a journey planning model, a vehicle working condition electricity consumption model, a user portrait model and the like, input the charging static data and the charging dynamic data to the pre-constructed charging planning model, output an optimal charging planning strategy of the vehicle, collect and analyze the charging dynamic data, and quickly obtain a result through an AI model, and optimize the charging plan in real time.
Further, after the optimal charging planning strategy of the vehicle is output, the optimal charging planning strategy of the time can be uploaded, so that when other vehicles carry out charging planning, the optimal charging planning strategy of the current vehicle is referred to, and the corresponding charging piles are avoided, and therefore charging efficiency is improved.
Optionally, in one embodiment of the present application, inputting the charging static data and the charging dynamic data into a pre-built charging planning model, outputting an optimal charging planning strategy for the vehicle includes: calculating a linear weight planning strategy, a route shortest planning strategy and total route time consumption corresponding to the time-consuming shortest planning strategy by using a pre-constructed charging planning model; and selecting a trip planning strategy with the shortest total time consumption of the trip, and generating an optimal charging planning strategy based on the trip planning strategy with the shortest total time consumption of the trip.
In the actual execution process, the embodiment of the application can respectively calculate the linear weight planning strategy, the route shortest planning strategy and the total route time consumption corresponding to the time consumption shortest planning strategy, wherein the calculation method can be as follows:
JT (journey Total time consuming) ＝DT (time-consuming to travel) +CT (total time spent charging) ，
According to the formula, the total time spent on the journey of the vehicle is the sum of the total time spent on driving and the total time spent on charging, JT is required to be reduced, but the JT is only required to be reduced, but in the vehicle DT (time-consuming to travel) And CT (computed tomography) (total time spent charging) The vehicle speed and the average power consumption of hundred kilometers are positively correlated, and the higher the vehicle speed is, the higher the average power consumption of every hundred kilometers is, so that the longer the charging time is. The average power consumption AP per hundred kilometres is thus measured as an important parameter when performing the charge planning.
CT all (total time consumption) ＝CT c (time consuming charging) +CTw (charging wait time-consuming) ，
According to the above formula, the total charging time is the sum of the charging time and the waiting time, and the charging time and the AP Average power consumption of hundred kilometers Positively correlated, the charging waiting time and the service condition of the charging pile show correlation, and the higher the service frequency of the charging pile is, the longer the charging waiting time is.
CT c (time consuming charging) ＝AP (hundred kilometers average power consumption) * Mileage/charge rate of the battery,
the charging time is inversely related to the charging speed, the charging time is shorter when the charging speed is higher, and the quick charging is preferably selected to reduce the charging time when the charging planning is performed.
The key point of reducing DT is to ensure that the running speed of a vehicle is increased as much as possible under the condition of hundred kilometers of power consumption, model training is carried out at an algorithm end according to a large amount of data to obtain the optimal acceleration V, and the optimal acceleration V is pushed to a user through a server, such as a vehicle networking platform, in the running process of the vehicle, but the actual situation is complex, the vehicle cannot ensure constant running all the time, so that real-time interaction between the vehicle and the vehicle networking platform is required in the running process, and the vehicle networking platform calls a planning algorithm in real time in combination with the current running data of the vehicle to obtain the latest charging plan and pushes the latest charging plan to the vehicle.
The key of improving the charging speed and reducing the charging waiting time is that the cold door quick charging pile is preferably selected, and the charging pile data collected at the platform end in real time monitor the state of the charging pile, so that the charging pile recommended in the vehicle driving process preferably recommends the quick charging pile, and the cold door is slowly charged to reduce the charging time consumption.
And obtaining a journey time-consuming formula according to the analysis:
JT (journey Total time consuming) ＝M (Total mileage) /V (optimal running speed) +(AP (hundred kilometers average power consumption) *M (Total mileage) /S (charging speed) )+CT w (waiting time for charging) ，
Therefore, the charging times are not smaller and better, the embodiment of the application can select the journey planning strategy with the shortest total journey time consumption, and generate the optimal charging planning strategy based on the journey planning strategy with the shortest total journey time consumption.
Optionally, in one embodiment of the present application, after outputting the optimal charging planning strategy of the vehicle, the method further includes: pushing an optimal charging planning strategy to a user; and controlling at least one acoustic reminding device and/or at least one optical reminding device of the vehicle to carry out charging reminding based on the optimal charging planning strategy.
As a possible implementation manner, the embodiment of the present application may push the optimal charging planning policy to the user, apply the optimal charging planning policy based on the selection of the user, and control at least one acoustic reminding device and/or at least one optical reminding device of the vehicle to perform charging reminding when the vehicle needs to be charged.
The working principle of the vehicle charging planning method according to the embodiment of the present application will be described in detail with reference to fig. 2.
Taking the internet of vehicles platform as a server, as shown in fig. 2, the embodiment of the application may include the following three steps:
s1: according to the method and the device for charging the vehicle, the charging planning request from the user or the vehicle can be received, the relevant information of the user is further queried, static charging data and dynamic charging data such as charging pile information and user portrait information are obtained, an optimal charging planning strategy is generated through calculation, and the optimal charging planning strategy is sent to the vehicle to be displayed.
S2: when the running data of the vehicle changes or the user changes the optimal charging planning strategy, the embodiment of the application can report the running data of the vehicle and the change information of the user in real time, calculate the average mileage value of the vehicle, such as average power consumption, average speed per hour and driving intensity, further judge whether the vehicle deviates from the optimal charging planning strategy, re-plan the vehicle if the vehicle deviates, and remind the user to charge when charging is needed if the vehicle does not deviate.
S3: after the mileage is over, the embodiment of the application can calculate the mean value data of the current path, and take the data as learning sample data to perform model training (programming algorithm training) and user portrait training.
The embodiment of the application can bear the capabilities of data acquisition, analysis and sharing, performs user portrait analysis on an owner of the vehicle, performs vehicle condition collection feedback capability on the vehicle, performs real-time use condition monitoring capability on the charging pile, and provides the latest and most accurate charging pile use condition for the owner of the platform vehicle. Basic information of a vehicle owner and a vehicle is mainly collected and reported through a vehicle Tbox and various vehicle body domain sensors, accurate use information of the charging piles is obtained, and accurate data collection and prediction are carried out on the use condition of each charging pile according to user reporting and a platform on the basis of charging planning of all users.
When a user needs to use the vehicle for a long distance, firstly, route planning is carried out on a starting point and a terminal point, a topological graph is formed by various routes from the starting point to the terminal point, a traffic hub from the starting point to the terminal point needs to pass through is a node in the topological graph, the number of charging piles between any two nodes is the weight of a line between the two nodes, a depth traversing algorithm is used, the optimal running route of the vehicle is calculated according to the weight proportion of the three with the largest weight, the shortest route and the shortest time consumption to serve as navigation information of the vehicle owner, all charging piles in the route are incorporated into the current charging planning, all possible running factors are collectively incorporated into a reference coordinate system in the initial planning process, and the initial planning is only the final charging planning of the vehicle running, so that the extreme situation that interaction with a vehicle networking platform cannot be carried out is prevented.
In the using process of the vehicle, the platform and the vehicle interact in real time through the Tbox, the real-time power consumption condition of the vehicle is calculated in an accumulated calculation mode, the power consumption condition of the vehicle in the next time is predicted by combining the user portrait, and then a charging plan is recommended to the user in real time according to the current and future using conditions of the charging pile.
The real-time interaction between the vehicle and the server is guaranteed by dynamic charging planning, the vehicle is interacted with the server continuously, the server continuously adjusts the planning according to real-time data of the vehicle, and the vehicle is pushed to the vehicle in real time when a reasonable charging plan exists, so that the vehicle is recommended to be charged even under the condition that the electric quantity state of the vehicle is good, and the time consumption of the whole mileage is reduced.
According to the vehicle charging planning method provided by the embodiment of the application, the charging static data corresponding to the destination and the vehicle charging dynamic data can be acquired in a targeted manner based on the destination and the charging planning request of the user, so that the rationality of charging planning is improved through fully utilizing the actual vehicle environment parameters, and the optimal charging planning strategy of the vehicle is obtained through inputting the charging static data and the charging dynamic data into the pre-built charging planning model, so that the charging planning is carried out according to the real working condition of the vehicle and the dynamic working condition of the charging pile, the planning result is more efficient, and the problem of mileage anxiety when the user drives the new energy vehicle for a long distance can be effectively linked. Therefore, the technical problems that in the related art, the charging planning cannot be adjusted in real time according to the actual working condition of the vehicle and the dynamic working condition of the charging pile, the application of the actual vehicle environment parameters is lacking and the like are solved.
Next, a charging planning apparatus for a vehicle according to an embodiment of the present application will be described with reference to the accompanying drawings.
Fig. 3 is a block schematic diagram of a charging planning apparatus of a vehicle according to an embodiment of the present application.
As shown in fig. 3, the charging programming device 10 of the vehicle is applied to a server, wherein the device 10 includes: a receiving module 100, an obtaining module 200 and a planning module 300.
Specifically, the receiving module 100 is configured to receive a destination and a charging planning request of a user.
And the obtaining module 200 is configured to obtain, based on the charging planning request, charging static data corresponding to the destination and charging dynamic data of the vehicle.
The planning module 300 is configured to input the charging static data and the charging dynamic data into a pre-constructed charging planning model, and output an optimal charging planning strategy of the vehicle, where the pre-constructed charging planning model is obtained by a charging pile usage model, a journey planning model, a vehicle condition electricity consumption model and/or a user portrait model.
Optionally, in one embodiment of the present application, the obtaining module 200 includes: a first generation unit and an acquisition unit.
The first generation unit is used for acquiring current position information of the vehicle, planning a journey based on the current position information and a destination, and generating at least one journey planning strategy.
And the acquisition unit is used for acquiring the corresponding charging static data based on each trip planning strategy.
Optionally, in one embodiment of the present application, the trip planning strategy includes a linear weight planning strategy, a route shortest planning strategy, and a time-consuming shortest planning strategy.
Optionally, in one embodiment of the present application, the planning module 300 includes: a computing unit and a second generating unit.
The calculation unit is used for calculating a linear weight planning strategy, a route shortest planning strategy and total route time consumption corresponding to the time-consuming shortest planning strategy by using a pre-constructed charging planning model.
And the second generation unit is used for selecting the journey planning strategy with the shortest journey total time consumption and generating the optimal charging planning strategy based on the journey planning strategy with the shortest journey total time consumption.
Optionally, in one embodiment of the present application, the charging planning device 10 of the vehicle further includes: and the pushing module and the control module.
The pushing module is used for pushing the optimal charging planning strategy to the user.
And the control module is used for controlling at least one acoustic reminding device and/or at least one optical reminding device of the vehicle to carry out charging reminding based on the optimal charging planning strategy.
It should be noted that the foregoing explanation of the embodiment of the method for planning the charging of the vehicle is also applicable to the device for planning the charging of the vehicle of this embodiment, and will not be repeated here.
According to the vehicle charging planning device provided by the embodiment of the application, the charging static data corresponding to the destination and the vehicle charging dynamic data can be acquired in a targeted manner based on the destination and the charging planning request of the user, so that the rationality of charging planning is improved through fully utilizing the environment parameters of the actual vehicle, and the optimal charging planning strategy of the vehicle is obtained through inputting the charging static data and the charging dynamic data into the pre-built charging planning model, so that the charging planning is carried out according to the real working condition of the vehicle and the dynamic working condition of the charging pile, the planning result is more efficient, and the problem of mileage anxiety when the user drives the new energy automobile for a long distance can be effectively linked. Therefore, the technical problems that in the related art, the charging planning cannot be adjusted in real time according to the actual working condition of the vehicle and the dynamic working condition of the charging pile, the application of the actual vehicle environment parameters is lacking and the like are solved.
Fig. 4 is a schematic structural diagram of a server according to an embodiment of the present application. The server may include:
The processor 402 implements the charge planning method of the vehicle provided in the above-described embodiment when executing the program.
Further, the server further includes:
a communication interface 403 for communication between the memory 401 and the processor 402.
A memory 401 for storing a computer program executable on the processor 402.
If the memory 401, the processor 402, and the communication interface 403 are implemented independently, the communication interface 403, the memory 401, and the processor 402 may be connected to each other by a bus and perform communication with each other. The bus may be an industry standard architecture (Industry Standard Architecture, abbreviated ISA) bus, an external device interconnect (Peripheral Component, abbreviated PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, abbreviated EISA) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, only one thick line is shown in fig. 4, but not only one bus or one type of bus.
Alternatively, in a specific implementation, if the memory 401, the processor 402, and the communication interface 403 are integrated on a chip, the memory 401, the processor 402, and the communication interface 403 may complete communication with each other through internal interfaces.
The processor 402 may be a central processing unit (Central Processing Unit, abbreviated as CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, abbreviated as ASIC), or one or more integrated circuits configured to implement embodiments of the present application.
The present embodiment also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of charging planning for a vehicle as described above.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "N" is at least two, such as two, three, etc., unless explicitly defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order from that shown or discussed, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or N wires, a portable computer cartridge (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. Although embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.
Claims (10)
1. A method of charging planning for a vehicle, characterized by being applied to a server, wherein the method comprises the steps of:
receiving a destination and a charging planning request of a user;
acquiring charging static data corresponding to the destination and charging dynamic data of the vehicle based on the charging planning request; and
and inputting the charging static data and the charging dynamic data into a pre-built charging planning model, and outputting an optimal charging planning strategy of the vehicle, wherein the pre-built charging planning model is obtained by a charging pile use model, a journey planning model, a vehicle working condition power consumption model and/or a user portrait model.
2. The method of claim 1, wherein the obtaining, based on the charging plan request, charging static data and charging dynamic data of the vehicle corresponding to the destination includes:
acquiring current position information of the vehicle, and planning a journey based on the current position information and the destination to generate at least one journey planning strategy;
and acquiring corresponding charging static data based on each trip planning strategy.
3. The method of claim 2, wherein the trip planning strategy comprises a linear weight planning strategy, a route shortest planning strategy, and a time-consuming shortest planning strategy.
4. A method according to claim 3, wherein said inputting said charge static data and said charge dynamic data into a pre-built charge planning model, outputting an optimal charge planning strategy for said vehicle, comprises:
calculating the linear weight planning strategy, the route shortest planning strategy and the total route time consumption corresponding to the time consumption shortest planning strategy by using a pre-constructed charging planning model;
and selecting a trip planning strategy with the shortest total time consumption of the trip, and generating the optimal charging planning strategy based on the trip planning strategy with the shortest total time consumption of the trip.
5. The method of claim 1, further comprising, after outputting the optimal charge planning strategy for the vehicle:
pushing the optimal charging planning strategy to a user;
and controlling at least one acoustic reminding device and/or at least one optical reminding device of the vehicle to carry out charging reminding based on the optimal charging planning strategy.
6. A charging programming device for a vehicle, applied to a server, wherein the device comprises:
the receiving module is used for receiving the destination and the charging planning request of the user;
the acquisition module is used for acquiring the charging static data corresponding to the destination and the charging dynamic data of the vehicle based on the charging planning request; and
and the planning module is used for inputting the charging static data and the charging dynamic data into a pre-constructed charging planning model and outputting an optimal charging planning strategy of the vehicle, wherein the pre-constructed charging planning model is obtained by a charging pile use model, a journey planning model, a vehicle working condition power consumption model and/or a user portrait model.
7. The apparatus of claim 6, wherein the acquisition module comprises:
the generation unit is used for acquiring the current position information of the vehicle, planning a journey based on the current position information and the destination, and generating at least one journey planning strategy;
and the acquisition unit is used for acquiring the corresponding charging static data based on each trip planning strategy.
8. The apparatus as recited in claim 6, further comprising:
the pushing module is used for pushing the optimal charging planning strategy to a user;
and the control module is used for controlling at least one acoustic reminding device and/or at least one optical reminding device of the vehicle to carry out charging reminding based on the optimal charging planning strategy.
9. A vehicle, characterized by comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the method of charging planning for a vehicle as claimed in any one of claims 1 to 5.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the program is executed by a processor for implementing a method of charging planning for a vehicle according to any one of claims 1-5.
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| CN202310119684.6A CN116029450A (en) | 2023-02-03 | 2023-02-03 | Vehicle charging planning method, device, server and storage medium |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202310119684.6A CN116029450A (en) | 2023-02-03 | 2023-02-03 | Vehicle charging planning method, device, server and storage medium |
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