Electric vehicle energy consumption early warning method, device, medium and equipment

Abstract

The present invention discloses an electric vehicle energy consumption early warning method, device, medium, and equipment. The method includes: responding to start and end point information, performing path planning to obtain a number of preliminary routes as a preliminary route set, performing congestion prediction processing on the preliminary route set to obtain congestion prediction information for the preliminary route set; calculating operation information corresponding to the preliminary route set based on the congestion prediction information of the preliminary route set and historical traffic information; inputting the operation information into an electric vehicle energy consumption model to obtain an energy consumption set output by the electric vehicle energy consumption model; and determining whether there is at least one preliminary route in the preliminary route set that meets preset feasible conditions based on the energy consumption set and the remaining power of the electric vehicle. If not, outputting an early warning message and modifying and delaying the departure time in the start and end point information to calculate a delayed travel route. The present invention can provide relevant early warnings based on more accurate electric vehicle energy consumption estimates.

Description

Technical Field

[0001] The present invention relates to the field of energy consumption early warning, and in particular to an electric vehicle energy consumption early warning method, device, medium and equipment. Background Art

[0002] Early warning of electric vehicle energy consumption is crucial for improving the user experience. Electric vehicle power consumption is significantly affected by operating conditions and external factors such as temperature. In particular, electric vehicle power consumption exhibits nonlinear characteristics. When driving in complex environments, this nonlinearity can affect driver judgment and reduce travel reliability.

[0003] In the existing technology, energy consumption warnings for electric vehicles usually use an energy consumption model based on power consumption during driving to estimate the mileage that can be traveled with the current power. The driver queries the remaining mileage information through the instrument panel. However, due to the nonlinear characteristics of power consumption due to other factors, the remaining mileage information is not accurate enough, which affects the driver's travel predictions. Summary of the Invention

[0004] In order to solve the above technical problems, embodiments of the present invention provide an electric vehicle energy consumption early warning method, device, medium and equipment.

[0005] An embodiment of the present invention provides an electric vehicle energy consumption early warning method, comprising:

[0006] In response to the start and end point information input by the user, route planning is performed to obtain a plurality of preliminary routes as a preliminary route set, and congestion prediction processing is performed on the preliminary route set to obtain congestion prediction information of the preliminary route set;

[0007] Calculating operation information corresponding to the preliminary route set based on the congestion prediction information of the preliminary route set and preset historical traffic information; wherein the operation information includes speed distribution and operation time of the electric vehicle on each of the preliminary routes;

[0008] Inputting the operation information into a preset electric vehicle energy consumption model to obtain an energy consumption set output by the electric vehicle energy consumption model; wherein the energy consumption set includes the energy consumption of each of the preliminary routes;

[0009] Based on the energy consumption set and the obtained remaining power of the electric vehicle, it is determined whether there is at least one preliminary route in the preliminary route set that meets the preset feasible conditions. If not, a warning message is output, and the departure time in the starting and ending point information is modified and delayed to calculate a delayed travel route.

[0010] Furthermore, before inputting the operating information into a preset electric vehicle energy consumption model to obtain the energy consumption set output by the electric vehicle energy consumption model, it also includes: obtaining current environmental information of each of the preliminary routes; wherein the current environmental information includes current weather and current ambient temperature; and correcting the operating information according to the current environmental information of each of the preliminary routes and a preset environmental road condition plan, so as to redefine the corrected operating information as new operating information.

[0011] Furthermore, the method performs route planning in response to the start and end point information input by the user to obtain several preliminary routes as a preliminary route set, and performs congestion prediction processing on the preliminary route set to obtain congestion prediction information for the preliminary route set, specifically including: using a pre-configured navigation map to perform route planning based on the start and end point information to obtain several preliminary routes; searching for historical route congestion data corresponding to each of the preliminary routes from preset historical congestion data; and performing congestion prediction processing on each of the preliminary routes based on the congestion data of each historical route and a preset prediction algorithm to obtain congestion prediction information for each of the preliminary routes.

[0012] Furthermore, the operation information corresponding to the preliminary route set is calculated based on the congestion prediction information of the preliminary route set and the preset historical traffic information, specifically including: searching for historical route traffic information corresponding to each of the preliminary routes from the historical traffic information; wherein the historical route traffic information includes the historical congestion level and historical average speed of the electric vehicle on each of the preliminary routes; based on each of the historical route traffic information and the length of the corresponding preliminary route, the operation information corresponding to each of the preliminary routes is calculated.

[0013] Furthermore, the electric vehicle energy consumption model is:

[0014] E c =E m +E s +E a +E other

[0015]

[0016]

[0017] E a =T w f(w)

[0018] Among them, E c is the energy consumption of each preliminary route, E m is the electric energy consumption in driving state, E s is the power consumption in idle state, Ea is the internal power consumption, E other For other power consumption, is the driving electric energy consumption coefficient at the current ambient temperature w, w m is the driving power consumption coefficient under the current weather conditions, v i is the speed distribution of the initial route of the i-th segment, t i is the running time of the initial route of the i-th segment, e m is the energy consumption per unit distance in the moving state, β w is the idle power consumption coefficient at the current ambient temperature w, w s is the idle power consumption coefficient under the current weather conditions, t si is the idling time of the i-th preliminary route, e s is the energy consumption per unit time in the idling state, T w is the operating time at the current ambient temperature w, and f(w) is the internal power consumption per unit time at the current ambient temperature w.

[0019] Furthermore, the feasible condition includes that the energy consumption is less than the remaining power of the electric vehicle; then, based on the energy consumption set and the obtained remaining power of the electric vehicle, it is determined whether there is at least one preliminary route in the preliminary route set that meets the preset feasible condition. If not, a warning message is output, and the departure time in the start and end point information is modified and postponed to calculate the delayed travel route, specifically including: if it exists, selecting the preliminary route that meets the feasible condition and has the shortest corresponding running time as the commutable route, and outputting the commutable route; if it does not exist, a warning message is output, and the departure time in the start and end point information is modified and postponed to recalculate a new energy consumption set, until at least one preliminary route that meets the feasible condition is selected as the delayed travel route.

[0020] Furthermore, the prediction algorithm includes a Conv-BiLSTM algorithm.

[0021] The present invention also provides an electric vehicle energy consumption early warning device, comprising:

[0022] a congestion prediction module, configured to perform route planning in response to the start and end point information input by the user, obtain a plurality of preliminary routes as a preliminary route set, and perform congestion prediction processing on the preliminary route set to obtain congestion prediction information for the preliminary route set;

[0023] an operation information calculation module, configured to calculate operation information corresponding to the preliminary route set based on the congestion prediction information of the preliminary route set and preset historical traffic information; wherein the operation information includes a speed distribution and operation time of the electric vehicle on each of the preliminary routes;

[0024] An energy consumption acquisition module, configured to input the operation information into a preset electric vehicle energy consumption model to obtain an energy consumption set output by the electric vehicle energy consumption model; wherein the energy consumption set includes the energy consumption of each of the preliminary routes;

[0025] The route selection module is used to determine whether there is at least one preliminary route that meets the preset feasibility conditions in the preliminary route set based on the energy consumption set and the obtained remaining power of the electric vehicle. If not, a warning message is output and the departure time in the start and end point information is modified and delayed to calculate a delayed travel route.

[0026] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the above-mentioned electric vehicle energy consumption warning methods.

[0027] An embodiment of the present invention also provides a computer device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the steps of any one of the above-mentioned electric vehicle energy consumption warning methods when executing the computer program.

[0028] In summary, the present invention has the following beneficial effects:

[0029] By adopting the embodiments of the present invention, factors such as road congestion, weather, and ambient temperature can be comprehensively considered when conducting energy consumption warnings for electric vehicles, thereby achieving more accurate energy consumption estimates and issuing relevant warnings based on the estimated energy consumption, making electric vehicle travel more reliable. In addition, intelligent recommendations for electric vehicle travel plans can be made, and the most feasible travel plan can be recommended to drivers, effectively improving the travel experience of electric vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a flow chart of an embodiment of an electric vehicle energy consumption early warning method provided by the present invention;

[0031] Figure 2 This is a structural diagram of an embodiment of an electric vehicle energy consumption warning device provided by the present invention;

[0032] Figure 3 The figure is a flow chart of a specific embodiment of an electric vehicle energy consumption early warning method provided by the present invention. DETAILED DESCRIPTION

[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0034] See also Figure 1 , is a flow chart of an embodiment of the electric vehicle energy consumption early warning method provided by the present invention, the method includes steps S1 to S4, which are specifically as follows:

[0035] S1, in response to the start and end point information input by the user, performing route planning to obtain a number of preliminary routes as a preliminary route set, and performing congestion prediction processing on the preliminary route set to obtain congestion prediction information of the preliminary route set;

[0036] Preferably, the process of performing route planning in response to the start and end point information input by the user to obtain a number of preliminary routes as a preliminary route set, and performing congestion prediction processing on the preliminary route set to obtain congestion prediction information of the preliminary route set, specifically includes: using a pre-configured navigation map to perform route planning according to the start and end point information to obtain a number of preliminary routes; searching for historical route congestion data corresponding to each of the preliminary routes from preset historical congestion data; and performing congestion prediction processing on each of the preliminary routes based on the congestion data of each historical route and a preset prediction algorithm to obtain congestion prediction information for each of the preliminary routes.

[0037] As an improvement to the above solution, the prediction algorithm includes a Conv-BiLSTM algorithm.

[0038] For example, based on the input start and end point information, a preliminary route plan is planned by calling the navigation map API (AutoNavi, Baidu, etc.), and the historical congestion data of each route plan is retrieved. The congestion situation of the route sections in each plan is predicted based on the Conv-Bi LSTM algorithm (a deep learning model that integrates convolutional neural networks and bidirectional long short-term memory networks) or a time series prediction model.

[0039] S2, calculating operation information corresponding to the preliminary route set based on the congestion prediction information of the preliminary route set and preset historical traffic information; wherein the operation information includes speed distribution and operation time of the electric vehicle on each of the preliminary routes;

[0040] Preferably, the operation information corresponding to the preliminary route set is calculated based on the congestion prediction information of the preliminary route set and the preset historical traffic information, specifically including: searching for historical route traffic information corresponding to each of the preliminary routes from the historical traffic information; wherein the historical route traffic information includes the historical congestion level and the historical average speed of the electric vehicle on each of the preliminary routes; based on each of the historical route traffic information and the length of the corresponding preliminary route, the operation information corresponding to each of the preliminary routes is calculated.

[0041] For example, based on the congestion prediction results of each route plan, the operating speed distribution and operating time of the road sections it passes through are matched for each route plan (this part is calculated based on the historical traffic operation data of the corresponding road section. For example, when the congestion level is B, the historical average vehicle speed is about 10km / h, and the expected time to pass a road section with a length of 1km is 6min).

[0042] S3, inputting the operation information into a preset electric vehicle energy consumption model to obtain an energy consumption set output by the electric vehicle energy consumption model; wherein the energy consumption set includes the energy consumption of each of the preliminary routes;

[0043] Preferably, before inputting the operating information into a preset electric vehicle energy consumption model to obtain the energy consumption set output by the electric vehicle energy consumption model, it also includes: obtaining the current environmental information of each of the preliminary routes; wherein the current environmental information includes the current weather and the current ambient temperature; according to the current environmental information of each of the preliminary routes and the preset environmental road condition plan, correcting the operating information to redefine the corrected operating information as new operating information.

[0044] Preferably, the electric vehicle energy consumption model is:

[0045] E c =E m +E s +E a +E other

[0046]

[0047]

[0048] E a =T w f(w)

[0049] Among them, E c is the energy consumption of each preliminary route, E m is the electric energy consumption in driving state, E s is the power consumption in idle state, Ea is the internal power consumption, E other For other power consumption, is the driving electric energy consumption coefficient at the current ambient temperature w, w m is the driving power consumption coefficient under the current weather conditions, v i is the speed distribution of the initial route of the i-th segment, t i is the running time of the initial route of the i-th segment, e m is the energy consumption per unit distance in the moving state, β w is the idle power consumption coefficient at the current ambient temperature w, w s is the idle power consumption coefficient under the current weather conditions, t si is the idling time of the i-th preliminary route, e s is the energy consumption per unit time in the idling state, T w is the operating time at the current ambient temperature w, and f(w) is the internal power consumption per unit time at the current ambient temperature w.

[0050] For example, real-time weather and ambient temperature information is obtained, and the calculated speed distribution and running time are corrected according to the road condition plan under different weather conditions. The above calculation results are input into the electric vehicle energy consumption model, so that the electric vehicle energy consumption model selects energy consumption parameters according to the ambient temperature information, calculates the internal energy consumption of the vehicle (such as air conditioning energy consumption), and finally calculates the energy consumption of each route plan.

[0051] S4, based on the energy consumption set and the obtained remaining power of the electric vehicle, determining whether there is at least one preliminary route in the preliminary route set that meets the preset feasibility conditions; if not, outputting a warning message and modifying and delaying the departure time in the starting and ending point information to calculate a delayed travel route.

[0052] Preferably, the feasible condition includes that the energy consumption is less than the remaining power of the electric vehicle; then, based on the energy consumption set and the obtained remaining power of the electric vehicle, it is determined whether there is at least one preliminary route in the preliminary route set that meets the preset feasible condition. If not, a warning message is output, and the departure time in the start and end point information is modified and delayed to calculate the delayed travel route, specifically including: if it exists, selecting the preliminary route that meets the feasible condition and has the shortest corresponding running time as the commutable route, and outputting the commutable route; if it does not exist, a warning message is output, and the departure time in the start and end point information is modified and delayed to recalculate a new energy consumption set, until at least one preliminary route that meets the feasible condition is selected as the delayed travel route.

[0053] For example, if there is no achievable route plan with the current power level, the congestion prediction result is used to calculate the route plan after delayed departure, with each delay step being 5 minutes until a feasible plan is obtained, and the plan is pushed to the driver.

[0054] Correspondingly, an embodiment of the present invention further provides an electric vehicle energy consumption warning device, which can implement all the processes of the electric vehicle energy consumption warning method provided in the above embodiment.

[0055] See also Figure 2 , is a structural diagram of an embodiment of the electric vehicle energy consumption warning device provided by the present invention.

[0056] The congestion prediction module 101 is configured to perform route planning in response to the start and end point information input by the user, obtain a plurality of preliminary routes as a preliminary route set, and perform congestion prediction processing on the preliminary route set to obtain congestion prediction information for the preliminary route set;

[0057] An operation information calculation module 102 is configured to calculate operation information corresponding to the preliminary route set based on the congestion prediction information of the preliminary route set and preset historical traffic information; wherein the operation information includes a speed distribution and operation time of the electric vehicle on each of the preliminary routes;

[0058] The energy consumption acquisition module 103 is configured to input the operation information into a preset electric vehicle energy consumption model to obtain an energy consumption set output by the electric vehicle energy consumption model; wherein the energy consumption set includes the energy consumption of each of the preliminary routes;

[0059] The route selection module 104 is configured to determine whether there is at least one preliminary route in the preliminary route set that meets preset feasibility conditions based on the energy consumption set and the obtained remaining power of the electric vehicle. If not, a warning message is output and the departure time in the start and end point information is modified and delayed to calculate a delayed travel route.

[0060] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the above-mentioned electric vehicle energy consumption warning methods.

[0061] An embodiment of the present invention also provides a computer device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the steps of any one of the above-mentioned electric vehicle energy consumption warning methods when executing the computer program.

[0062] The computer device of this embodiment includes: a processor, a memory, and a computer program stored in the memory and capable of running on the processor, such as an electric vehicle energy consumption warning program. When the processor executes the computer program, the steps in the above-mentioned embodiments of the electric vehicle energy consumption warning method are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments, such as steps 101 to 104, are implemented.

[0063] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in the computer device.

[0064] The computer device may be a desktop computer, a notebook computer, a PDA, a cloud server, etc. The computer device may include, but is not limited to, a processor and a memory.

[0065] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the computer device, connecting various parts of the entire computer device using various interfaces and lines.

[0066] The memory can be used to store the computer programs and / or modules, and the processor implements various functions of the computer device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created based on the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0067] Wherein, if the module / unit integrated in the computer device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0068] In summary, the present invention has the following beneficial effects:

[0069] By adopting the embodiments of the present invention, factors such as road congestion, weather, and ambient temperature can be comprehensively considered when conducting energy consumption warnings for electric vehicles, thereby achieving more accurate energy consumption estimates and issuing relevant warnings based on the estimated energy consumption, making electric vehicle travel more reliable. In addition, intelligent recommendations for electric vehicle travel plans can be made, and the most feasible travel plan can be recommended to drivers, effectively improving the travel experience of electric vehicles.

[0070] Through the description of the above embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus the necessary hardware platform, and of course, it can also be implemented entirely by hardware. Based on this understanding, all or part of the contribution of the technical solution of the present invention to the background art can be embodied in the form of a software product. This computer software product can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments of the present invention or certain parts of the embodiments.

[0071] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for early warning of energy consumption of electric vehicles, characterized in that: include: In response to the start and end point information input by the user, route planning is performed to obtain a plurality of preliminary routes as a preliminary route set, and congestion prediction processing is performed on the preliminary route set to obtain congestion prediction information of the preliminary route set; Calculating operation information corresponding to the preliminary route set based on the congestion prediction information of the preliminary route set and preset historical traffic information; wherein the operation information includes speed distribution and operation time of the electric vehicle on each of the preliminary routes; Inputting the operation information into a preset electric vehicle energy consumption model to obtain an energy consumption set output by the electric vehicle energy consumption model; wherein the energy consumption set includes the energy consumption of each of the preliminary routes; Based on the energy consumption set and the obtained remaining power of the electric vehicle, determining whether there is at least one preliminary route in the preliminary route set that meets the preset feasibility conditions; if not, outputting a warning message and modifying and delaying the departure time in the start and end point information to calculate a delayed travel route; Wherein, before inputting the operation information into a preset electric vehicle energy consumption model to obtain an energy consumption set output by the electric vehicle energy consumption model, the method further includes: Acquiring current environmental information of each of the preliminary routes; wherein the current environmental information includes current weather and current ambient temperature; According to the current environmental information of each of the preliminary routes and a preset environmental road condition plan, the operation information is modified to re-determine the modified operation information as new operation information; The energy consumption model of the electric vehicle is: AND c =And m +E s +E a +E other E a =T w f(w) Among them, E c is the energy consumption of each preliminary route, E m is the electric energy consumption in driving state, E s is the power consumption in idle state, E a is the internal power consumption, E other For other power consumption, is the driving electric energy consumption coefficient at the current ambient temperature w, w m is the driving power consumption coefficient under the current weather conditions, v i is the speed distribution of the initial route of the i-th segment, t i is the running time of the initial route of the i-th segment, e m is the energy consumption per unit distance in the moving state, β w is the idle power consumption coefficient at the current ambient temperature w, w s is the idle power consumption coefficient under the current weather conditions, t si is the idling time of the i-th preliminary route, e s is the energy consumption per unit time in the idling state, T w is the operating time at the current ambient temperature w, and f(w) is the internal power consumption per unit time at the current ambient temperature w.

2. The electric vehicle energy consumption early warning method according to claim 1, characterized in that: The step of performing route planning in response to the start and end point information input by the user to obtain a plurality of preliminary routes as a preliminary route set, and performing congestion prediction processing on the preliminary route set to obtain congestion prediction information for the preliminary route set specifically includes: Using a pre-configured navigation map, path planning is performed based on the start and end point information to obtain several preliminary routes; searching for historical route congestion data corresponding to each of the preliminary routes from preset historical congestion data; Based on the congestion data of each of the historical routes and a preset prediction algorithm, congestion prediction processing is performed on each of the preliminary routes to obtain congestion prediction information for each of the preliminary routes.

3. The electric vehicle energy consumption early warning method according to claim 1, characterized in that: The calculating, based on the congestion prediction information of the preliminary route set and preset historical traffic information, operation information corresponding to the preliminary route set specifically includes: Searching for historical route traffic information corresponding to each of the preliminary routes from the historical traffic information; wherein the historical route traffic information includes a historical congestion level and a historical average speed of the electric vehicle on each of the preliminary routes; Based on the traffic information of each historical route and the length of the corresponding preliminary route, the operation information corresponding to each preliminary route is calculated.

4. The electric vehicle energy consumption early warning method according to claim 1, characterized in that: The feasibility conditions include that the energy consumption is less than the remaining power of the electric vehicle; Then, the method of determining whether there is at least one preliminary route that meets the preset feasible conditions in the preliminary route set based on the energy consumption set and the obtained remaining power of the electric vehicle, and if not, outputting a warning message and modifying and delaying the departure time in the start and end point information to calculate a delayed travel route, specifically includes: If so, the preliminary route that meets the feasibility conditions and has the shortest running time is selected as the commutable route, and the commutable route is output; If it does not exist, a warning message is output, and the departure time in the start and end point information is modified and postponed to recalculate a new energy consumption set until at least one preliminary route that meets the feasible conditions is selected as the delayed travel route.

5. The electric vehicle energy consumption early warning method according to claim 2, characterized in that: The prediction algorithm includes a Conv-Bi LSTM algorithm.

6. An electric vehicle energy consumption warning device, characterized in that: include: a congestion prediction module, configured to perform route planning in response to the start and end point information input by the user, obtain a plurality of preliminary routes as a preliminary route set, and perform congestion prediction processing on the preliminary route set to obtain congestion prediction information for the preliminary route set; an operation information calculation module, configured to calculate operation information corresponding to the preliminary route set based on the congestion prediction information of the preliminary route set and preset historical traffic information; wherein the operation information includes a speed distribution and operation time of the electric vehicle on each of the preliminary routes; An energy consumption acquisition module, configured to input the operation information into a preset electric vehicle energy consumption model to obtain an energy consumption set output by the electric vehicle energy consumption model; wherein the energy consumption set includes the energy consumption of each of the preliminary routes; a route selection module, configured to determine whether there is at least one preliminary route in the preliminary route set that meets preset feasibility conditions based on the energy consumption set and the acquired remaining power of the electric vehicle; if not, output a warning message and modify and postpone the departure time in the start and end point information to calculate a delayed travel route; Wherein, before inputting the operation information into a preset electric vehicle energy consumption model to obtain an energy consumption set output by the electric vehicle energy consumption model, the method further includes: Acquiring current environmental information of each of the preliminary routes; wherein the current environmental information includes current weather and current ambient temperature; According to the current environmental information of each of the preliminary routes and a preset environmental road condition plan, the operation information is modified to re-determine the modified operation information as new operation information; The energy consumption model of the electric vehicle is: AND c =And m +E s +E a +E other E a =T w f(w) Among them, E c is the energy consumption of each preliminary route, E m is the electric energy consumption in driving state, E s is the power consumption in idle state, E a is the internal power consumption, E other For other power consumption, is the driving electric energy consumption coefficient at the current ambient temperature w, w m is the driving power consumption coefficient under the current weather conditions, v i is the speed distribution of the i-th segment of the preliminary route, t i is the running time of the initial route of the i-th segment, e m is the energy consumption per unit distance in the moving state, β w is the idle power consumption coefficient at the current ambient temperature w, w s is the idle power consumption coefficient under the current weather conditions, t si is the idling time of the i-th preliminary route, e s is the energy consumption per unit time in the idling state, T w is the operating time at the current ambient temperature w, and f(w) is the internal power consumption per unit time at the current ambient temperature w.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the electric vehicle energy consumption early warning method according to any one of claims 1 to 5 is implemented.

8. A computer device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the electric vehicle energy consumption early warning method according to any one of claims 1 to 5 is implemented.

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