CN116945907B - New energy electric automobile mileage calculation method and system - Google Patents
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
- B60L3/12—Recording operating variables ; Monitoring of operating variables
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2260/00—Operating Modes
- B60L2260/40—Control modes
- B60L2260/50—Control modes by future state prediction
- B60L2260/52—Control modes by future state prediction drive range estimation, e.g. of estimation of available travel distance
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2260/00—Operating Modes
- B60L2260/40—Control modes
- B60L2260/50—Control modes by future state prediction
- B60L2260/54—Energy consumption estimation
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
Abstract
The invention provides a new energy electric automobile mileage calculation method and a system, wherein the method comprises the following steps: collecting a departure place and a destination of a vehicle driven by a user in real time, and analyzing a commute route of the user according to the departure place and the destination; acquiring weather information on a commute route in real time, and calculating a first loss value of the vehicle on the commute route according to the weather information; and detecting a second loss value of the vehicle-mounted electric appliance of the vehicle in real time through a preset sensor, and simultaneously inputting the first loss value and the second loss value into a trained mileage calculation model, so that the mileage calculation model outputs the residual endurance mileage corresponding to the vehicle in real time. The method and the device can accurately predict the remaining endurance mileage of the vehicle, and correspondingly improve the use experience of the user.
Description
Technical Field
The invention relates to the technical field of new energy electric vehicles, in particular to a new energy electric vehicle mileage calculation method and system.
Background
Along with the progress of technology and the rapid development of productivity, the technology of new energy electric automobiles is mature, and is accepted by people gradually, so that the new energy electric automobiles are popularized in daily life of people, and the life of people is greatly facilitated.
In order to enable a driver to clearly know the mileage that the vehicle can travel, the prior art calculates the remaining endurance mileage of the new energy electric vehicle in real time in an instrument panel so as to eliminate the mileage anxiety of the driver.
However, in the prior art, most of the residual mileage of the vehicle is calculated directly according to the electric quantity of the power battery pack in the new energy electric vehicle, and the calculated residual mileage is inaccurate due to single factors considered by the calculation mode, so that misjudgment can be brought to a driver, and the use experience of the driver is correspondingly reduced.
Disclosure of Invention
Based on the above, the invention aims to provide a new energy electric automobile mileage calculation method and system, so as to solve the problem that the driving experience of a user is reduced due to the fact that the calculated remaining mileage is inaccurate in the prior art.
The first aspect of the embodiment of the invention provides:
a new energy electric automobile mileage calculation method, wherein the method includes:
collecting a departure place and a destination of a vehicle driven by a user in real time, and analyzing a commute route of the user according to the departure place and the destination;
acquiring weather information on the commute route in real time, and calculating a first loss value of the vehicle on the commute route according to the weather information;
and detecting a second loss value of the vehicle-mounted electric appliance of the vehicle in real time through a preset sensor, and simultaneously inputting the first loss value and the second loss value into a trained mileage calculation model, so that the mileage calculation model outputs the residual endurance mileage corresponding to the vehicle in real time.
The beneficial effects of the invention are as follows: according to the method, the habit of driving the vehicle by the user can be correspondingly acquired through acquiring the commute route of the user in real time, based on the habit, the weather information on the commute route which is frequently driven by the user is acquired in real time, the influence of the weather on the endurance of the vehicle can be considered, further, the remaining endurance mileage corresponding to the current vehicle is output in real time according to the trained mileage calculation model, the accuracy is high, and meanwhile the driving experience of the user is greatly improved.
Further, the step of simultaneously inputting the first loss value and the second loss value into a trained mileage calculation model, so that the mileage calculation model outputs the remaining endurance mileage corresponding to the vehicle in real time includes:
acquiring performance parameters of the vehicle, and converting the performance parameters into corresponding performance vectors;
and carrying out coding processing on the performance vector so as to convert the performance vector into a corresponding coding vector, and inputting the coding vector into a preset GPT model so as to train initial parameters in the preset GPT model and generate the mileage calculation model.
Further, the step of training the initial parameters in the preset GPT model and generating the mileage calculation model includes:
generating a corresponding test data set according to the coding vector, and preprocessing the test data set;
dividing the test data set into a test set and a verification set according to a preset proportion, and inputting the test set into the preset GPT model to correspondingly train an initial mileage calculation model;
verifying the initial mileage calculation model through the verification set, and judging whether a verification result is qualified or not in real time;
and if the verification result is judged to be qualified in real time, finishing verification of the initial mileage calculation model so as to correspondingly generate the mileage calculation model.
Further, the step of inputting the test set into the preset GPT model to correspondingly train an initial mileage calculation model includes:
inputting the test set into an analysis layer of the preset GPT model, and analyzing the test set into corresponding training codes through the analysis layer, wherein the training codes consist of numbers and letters;
inputting the training codes into a training layer of the preset GPT model, and training original model parameters in the training layer through the training codes so as to adjust the original model parameters into target model parameters which are suitable for the vehicle;
and inputting the target model parameters into a storage layer of the preset GPT model to correspondingly train out the initial mileage calculation model.
Further, the step of verifying the initial mileage calculation model through the verification set includes:
iteratively inputting the verification set into the initial mileage calculation model so that the initial mileage calculation model correspondingly outputs a plurality of predicted values, and judging whether the difference values among the plurality of predicted values are within a preset threshold value one by one;
and if the difference value among the plurality of predicted values is judged to be within the preset threshold value in real time, setting the predicted value as the verification result.
Further, the method further comprises:
monitoring the change of the remaining range in real time, and judging whether the remaining range is larger than a preset range threshold in real time;
and if the residual continuous voyage mileage is judged to be smaller than the preset mileage threshold in real time, corresponding prompt information is sent out in an instrument panel of the vehicle, wherein the prompt information comprises a text prompt and an icon prompt.
Further, the method further comprises:
when the remaining endurance mileage is judged to be smaller than the preset mileage threshold value, detecting the running place of the current vehicle in real time, and searching a corresponding charging station around the running place;
and displaying the position of the charging station in the instrument panel of the vehicle in real time, and sending out a voice prompt.
A second aspect of an embodiment of the present invention proposes:
a new energy electric vehicle mileage calculation system, wherein the system comprises:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring a departure place and a destination of a vehicle driven by a user in real time and analyzing a commute route of the user according to the departure place and the destination;
the calculation module is used for acquiring weather information on the commute route in real time and calculating a first loss value of the vehicle on the commute route according to the weather information;
the output module is used for detecting a second loss value of the vehicle-mounted electrical appliance of the vehicle in real time through a preset sensor, and inputting the first loss value and the second loss value into the trained mileage calculation model at the same time, so that the mileage calculation model outputs the residual endurance mileage corresponding to the vehicle in real time.
Further, the output module is specifically configured to:
acquiring performance parameters of the vehicle, and converting the performance parameters into corresponding performance vectors;
and carrying out coding processing on the performance vector so as to convert the performance vector into a corresponding coding vector, and inputting the coding vector into a preset GPT model so as to train initial parameters in the preset GPT model and generate the mileage calculation model.
Further, the output module is specifically configured to:
generating a corresponding test data set according to the coding vector, and preprocessing the test data set;
dividing the test data set into a test set and a verification set according to a preset proportion, and inputting the test set into the preset GPT model to correspondingly train an initial mileage calculation model;
verifying the initial mileage calculation model through the verification set, and judging whether a verification result is qualified or not in real time;
and if the verification result is judged to be qualified in real time, finishing verification of the initial mileage calculation model so as to correspondingly generate the mileage calculation model.
Further, the output module is specifically further configured to:
inputting the test set into an analysis layer of the preset GPT model, and analyzing the test set into corresponding training codes through the analysis layer, wherein the training codes consist of numbers and letters;
inputting the training codes into a training layer of the preset GPT model, and training original model parameters in the training layer through the training codes so as to adjust the original model parameters into target model parameters which are suitable for the vehicle;
and inputting the target model parameters into a storage layer of the preset GPT model to correspondingly train out the initial mileage calculation model.
Further, the output module is specifically configured to:
iteratively inputting the verification set into the initial mileage calculation model so that the initial mileage calculation model correspondingly outputs a plurality of predicted values, and judging whether the difference values among the plurality of predicted values are within a preset threshold value one by one;
and if the difference value among the plurality of predicted values is judged to be within the preset threshold value in real time, setting the predicted value as the verification result.
Further, the new energy electric automobile mileage calculation system further comprises a monitoring module, wherein the monitoring module is specifically used for:
monitoring the change of the remaining range in real time, and judging whether the remaining range is larger than a preset range threshold in real time;
and if the residual continuous voyage mileage is judged to be smaller than the preset mileage threshold in real time, corresponding prompt information is sent out in an instrument panel of the vehicle, wherein the prompt information comprises a text prompt and an icon prompt.
Further, the new energy electric automobile mileage calculation system further comprises a judging module, wherein the judging module is specifically used for:
when the remaining endurance mileage is judged to be smaller than the preset mileage threshold value, detecting the running place of the current vehicle in real time, and searching a corresponding charging station around the running place;
and displaying the position of the charging station in the instrument panel of the vehicle in real time, and sending out a voice prompt.
A third aspect of an embodiment of the present invention proposes:
the computer comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor realizes the new energy electric vehicle mileage calculation method provided by the embodiment when executing the computer program.
A fourth aspect of the embodiment of the present invention proposes:
a readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements the new energy electric vehicle mileage calculation method provided in the above embodiment.
Additional aspects and advantages of the invention 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 invention.
Drawings
Fig. 1 is a flowchart of a mileage calculation method of a new energy electric vehicle according to a first embodiment of the present invention;
fig. 2 is a block diagram of a mileage calculation system of a new energy electric vehicle according to a sixth embodiment of the present invention.
The invention will be further described in the following detailed description in conjunction with the above-described figures.
Detailed Description
In order that the invention may be readily understood, a more complete description of the invention will be rendered by reference to the appended drawings. Several embodiments of the invention are presented in the figures. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
It will be understood that when an element is referred to as being "mounted" on another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. The terms "vertical," "horizontal," "left," "right," and the like are used herein for illustrative purposes only.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
First embodiment
Referring to fig. 1, a mileage calculation method for a new energy electric vehicle according to a first embodiment of the present invention is shown, and the mileage calculation method for the new energy electric vehicle according to the present embodiment can output a remaining range corresponding to a vehicle in real time according to a trained mileage calculation model, so that accuracy is high, and meanwhile, use experience of a user is greatly improved.
Specifically, the method for calculating the mileage of the new energy electric vehicle provided by the embodiment specifically includes the following steps:
step S10, collecting a departure place and a destination of a vehicle driven by a user in real time, and analyzing a commute route of the user according to the departure place and the destination;
step S20, weather information on the commute route is obtained in real time, and a first loss value of the vehicle on the commute route is calculated according to the weather information;
specifically, in the embodiment, it is first to be described that the mileage calculation method is specifically applied to various new energy electric vehicles with different models, and is used for accurately calculating the remaining mileage of the vehicle in real time, so as to eliminate the mileage anxiety of the driver. Based on the above, it is required to collect the departure place and destination of the vehicle driven by the user in real time during the daily driving of the vehicle by the driver, wherein the daily commute route of each user is determined according to the frequency of occurrence of the departure place and destination, and meanwhile, the weather information on the current commute route of the user is obtained in real time based on the current weather information database, and the first loss value of the current vehicle on the commute route is further calculated according to the weather information.
When calculating the first loss value of the current vehicle on the commute route according to the weather information, the weather information on the commute route of the vehicle needs to be acquired first, so that the wet slip coefficient, the wind resistance coefficient and the like of the road are determined, the performance condition of the vehicle under the current scene, including the acceleration performance, the braking performance and the like, is calculated based on the weather information, and the performance condition is compared with the initial performance of the vehicle, so that the first loss value of the current vehicle on the commute route is obtained.
And step S30, detecting a second loss value of the vehicle-mounted electric appliance of the vehicle in real time through a preset sensor, and simultaneously inputting the first loss value and the second loss value into a trained mileage calculation model so that the mileage calculation model outputs the residual endurance mileage corresponding to the vehicle in real time.
Further, it is also necessary to detect the second loss value required to be consumed in real time by the vehicle-mounted electrical appliance in the current vehicle in real time by various sensors preset in the current vehicle in real time, specifically, by a current sensor, a voltage sensor, a temperature sensor, and the like. After the first loss value and the second loss value are respectively obtained through the steps, the first loss value and the second loss value are immediately and simultaneously input into a pre-trained mileage calculation model, and on the basis, the mileage calculation model can correspondingly output the remaining endurance mileage corresponding to the vehicle in real time.
Wherein,represents the remaining endurance mileage, x represents the first loss value, mu represents the mileage coefficient, sigma 2 Mean square error, epsilon, second loss value.
Second embodiment
Specifically, in this embodiment, it should be noted that, the step of inputting the first loss value and the second loss value into the trained mileage calculation model at the same time, so that the mileage calculation model outputs the remaining endurance mileage corresponding to the vehicle in real time includes:
acquiring performance parameters of the vehicle, and converting the performance parameters into corresponding performance vectors;
and carrying out coding processing on the performance vector so as to convert the performance vector into a corresponding coding vector, and inputting the coding vector into a preset GPT model so as to train initial parameters in the preset GPT model and generate the mileage calculation model.
Specifically, in this embodiment, it should be noted that, in order to accurately calculate the real-time endurance mileage of the current vehicle, the mileage calculation model needs to be accurately constructed first, based on which a required performance parameter needs to be obtained according to the signal correspondence of each vehicle, and specifically, the performance parameter is composed of a series of numerical values and letters, and meanwhile, the performance parameter is converted into a corresponding performance vector in real time.
Further, the current performance vector is encoded into an encoded vector which can be identified by a computer, and the encoded vector is input into a preset GPT model in real time so as to finally train the mileage calculation model.
Specifically, in this embodiment, it should be further noted that the step of training the initial parameters in the preset GPT model and generating the mileage calculation model includes:
generating a corresponding test data set according to the coding vector, and preprocessing the test data set;
dividing the test data set into a test set and a verification set according to a preset proportion, and inputting the test set into the preset GPT model to correspondingly train an initial mileage calculation model;
verifying the initial mileage calculation model through the verification set, and judging whether a verification result is qualified or not in real time;
and if the verification result is judged to be qualified in real time, finishing verification of the initial mileage calculation model so as to correspondingly generate the mileage calculation model.
Specifically, in this embodiment, it should be further noted that after the required encoding vector is obtained through the above steps, a corresponding test data set can be generated, and meanwhile, the corresponding test set and the verification set are split according to a ratio of 7:3, and training is performed on the GPT model through the test set, further, corresponding verification is performed through the verification set, and after the verification result is judged to be qualified, the mileage calculation model is immediately generated.
The model is constructed based on a CNN neural network, specifically, a corresponding training set and a corresponding verification set can be constructed according to the acquired weather information and the vehicle commute route, and further, the training set is input into a coding layer, an analysis layer and a learning layer of the CNN neural network to finally finish adjustment and training of original network parameters in the learning layer, and verification is carried out through the verification set to train the mileage calculation model.
Third embodiment
In addition, in this embodiment, it should be noted that, the step of inputting the test set into the preset GPT model to correspondingly train the initial mileage calculation model includes:
inputting the test set into an analysis layer of the preset GPT model, and analyzing the test set into corresponding training codes through the analysis layer, wherein the training codes consist of numbers and letters;
inputting the training codes into a training layer of the preset GPT model, and training original model parameters in the training layer through the training codes so as to adjust the original model parameters into target model parameters which are suitable for the vehicle;
and inputting the target model parameters into a storage layer of the preset GPT model to correspondingly train out the initial mileage calculation model.
In addition, in this embodiment, it should be noted that, by the above manner, the analysis layer can analyze the training code of the preset GPT model, that is, the training code that can be recognized by the computer, and at the same time, the training code is input into the training layer of the preset GPT model, so that the original model parameters in the current training layer can be trained.
Furthermore, the original model parameters in the preset GPT model can be adjusted to the required target model parameters, and meanwhile, the target model parameters are stored in a preset storage layer to complete training.
In addition, in this embodiment, it should be further noted that the step of verifying the initial mileage calculation model through the verification set includes:
iteratively inputting the verification set into the initial mileage calculation model so that the initial mileage calculation model correspondingly outputs a plurality of predicted values, and judging whether the difference values among the plurality of predicted values are within a preset threshold value one by one;
and if the difference value among the plurality of predicted values is judged to be within the preset threshold value in real time, setting the predicted value as the verification result.
In addition, in this embodiment, it should be further noted that after the initial mileage calculation model is constructed through the above steps, a corresponding verification process is required at this time, specifically, the verification set is input into the current initial mileage calculation model, and whether the output verification result meets a preset threshold, that is, whether the accuracy of the output mileage is higher than the preset threshold is determined in real time, so as to finally construct the mileage calculation model.
Fourth embodiment
In this embodiment, it should be noted that, the method further includes:
monitoring the change of the remaining range in real time, and judging whether the remaining range is larger than a preset range threshold in real time;
and if the residual continuous voyage mileage is judged to be smaller than the preset mileage threshold in real time, corresponding prompt information is sent out in an instrument panel of the vehicle, wherein the prompt information comprises a text prompt and an icon prompt.
In this embodiment, it is noted that, by monitoring the change of the remaining range of the vehicle in real time, the running state of the vehicle can be obtained in real time.
Further, whether the remaining range is larger than a preset range threshold value or not is judged in real time, so that corresponding prompt information can be accurately sent out, and a driver can timely improve the range of the vehicle.
In this embodiment, it should be noted that, the method further includes:
when the remaining endurance mileage is judged to be smaller than the preset mileage threshold value, detecting the running place of the current vehicle in real time, and searching a corresponding charging station around the running place;
and displaying the position of the charging station in the instrument panel of the vehicle in real time, and sending out a voice prompt.
In this embodiment, it is noted that, in order to enable a driver to timely charge a vehicle, the current vehicle is positioned in real time, and whether a charging station exists around the current vehicle is searched in real time.
Further, if the real-time position of the charging station is found, the real-time position of the charging station is sent to an instrument panel of the current vehicle, so that a driver can charge in time.
Fifth embodiment
Referring to fig. 2, a fifth embodiment of the present invention provides:
a new energy electric vehicle mileage calculation system, wherein the system comprises:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring a departure place and a destination of a vehicle driven by a user in real time and analyzing a commute route of the user according to the departure place and the destination;
the calculation module is used for acquiring weather information on the commute route in real time and calculating a first loss value of the vehicle on the commute route according to the weather information;
the output module is used for detecting a second loss value of the vehicle-mounted electrical appliance of the vehicle in real time through a preset sensor, and inputting the first loss value and the second loss value into the trained mileage calculation model at the same time, so that the mileage calculation model outputs the residual endurance mileage corresponding to the vehicle in real time.
In the mileage calculation system of the new energy electric vehicle, the output module is specifically configured to:
acquiring performance parameters of the vehicle, and converting the performance parameters into corresponding performance vectors;
and carrying out coding processing on the performance vector so as to convert the performance vector into a corresponding coding vector, and inputting the coding vector into a preset GPT model so as to train initial parameters in the preset GPT model and generate the mileage calculation model.
In the mileage calculation system of the new energy electric vehicle, the output module is specifically configured to:
generating a corresponding test data set according to the coding vector, and preprocessing the test data set;
dividing the test data set into a test set and a verification set according to a preset proportion, and inputting the test set into the preset GPT model to correspondingly train an initial mileage calculation model;
verifying the initial mileage calculation model through the verification set, and judging whether a verification result is qualified or not in real time;
and if the verification result is judged to be qualified in real time, finishing verification of the initial mileage calculation model so as to correspondingly generate the mileage calculation model.
In the mileage calculation system of the new energy electric vehicle, the output module is further specifically configured to:
inputting the test set into an analysis layer of the preset GPT model, and analyzing the test set into corresponding training codes through the analysis layer, wherein the training codes consist of numbers and letters;
inputting the training codes into a training layer of the preset GPT model, and training original model parameters in the training layer through the training codes so as to adjust the original model parameters into target model parameters which are suitable for the vehicle;
and inputting the target model parameters into a storage layer of the preset GPT model to correspondingly train out the initial mileage calculation model.
In the mileage calculation system of the new energy electric vehicle, the output module is specifically configured to:
iteratively inputting the verification set into the initial mileage calculation model so that the initial mileage calculation model correspondingly outputs a plurality of predicted values, and judging whether the difference values among the plurality of predicted values are within a preset threshold value one by one;
and if the difference value among the plurality of predicted values is judged to be within the preset threshold value in real time, setting the predicted value as the verification result.
Among the above-mentioned new energy electric automobile mileage calculation system, new energy electric automobile mileage calculation system still includes monitoring module, monitoring module specifically is used for:
monitoring the change of the remaining range in real time, and judging whether the remaining range is larger than a preset range threshold in real time;
and if the residual continuous voyage mileage is judged to be smaller than the preset mileage threshold in real time, corresponding prompt information is sent out in an instrument panel of the vehicle, wherein the prompt information comprises a text prompt and an icon prompt.
In the mileage calculation system of the new energy electric vehicle, the mileage calculation system of the new energy electric vehicle further comprises a judging module, wherein the judging module is specifically used for:
when the remaining endurance mileage is judged to be smaller than the preset mileage threshold value, detecting the running place of the current vehicle in real time, and searching a corresponding charging station around the running place;
and displaying the position of the charging station in the instrument panel of the vehicle in real time, and sending out a voice prompt.
Sixth embodiment
The sixth embodiment of the invention provides a computer, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the mileage calculation method of the new energy electric vehicle provided by the embodiment when executing the computer program.
Seventh embodiment
A seventh embodiment of the present invention provides a readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements the new energy electric vehicle mileage calculation method provided in the above embodiment.
In summary, the method and the system for calculating the mileage of the new energy electric vehicle provided by the embodiment of the invention can output the remaining endurance mileage corresponding to the current vehicle in real time according to the trained mileage calculation model, have higher accuracy, and greatly improve the driving experience of the user.
The above-described respective modules may be functional modules or program modules, and may be implemented by software or hardware. For modules implemented in hardware, the various modules described above may be located in the same processor; or the above modules may be located in different processors in any combination.
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 more wires, a portable computer diskette (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, for instance, 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 invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, 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.
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 invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (6)
1. The method for calculating the mileage of the new energy electric vehicle is characterized by comprising the following steps of:
collecting a departure place and a destination of a vehicle driven by a user in real time, and analyzing a commute route of the user according to the departure place and the destination;
acquiring weather information on the commute route in real time, and calculating a first loss value of the vehicle on the commute route according to the weather information, wherein the weather information on the vehicle commute route is acquired, a wet slip coefficient and a wind resistance coefficient of a road are determined, performance conditions of the vehicle under the current situation, including acceleration performance and braking performance, are calculated based on the weather information, and compared with initial performance of the vehicle to obtain the first loss value of the vehicle on the commute route;
detecting a second loss value of the vehicle-mounted electric appliance of the vehicle in real time through a preset sensor, and simultaneously inputting the first loss value and the second loss value into a trained mileage calculation model to enable the mileage calculation model to output the remaining endurance mileage corresponding to the vehicle in real time;
the step of simultaneously inputting the first loss value and the second loss value into a trained mileage calculation model to enable the mileage calculation model to output the remaining endurance mileage corresponding to the vehicle in real time includes:
acquiring performance parameters of the vehicle, and converting the performance parameters into corresponding performance vectors;
performing coding processing on the performance vector to convert the performance vector into a corresponding coding vector, and inputting the coding vector into a preset GPT model to train initial parameters in the preset GPT model and generate the mileage calculation model, wherein the expression of the mileage calculation model is as follows:
wherein,represents the remaining endurance mileage, x represents the first loss value, mu represents the mileage coefficient, sigma 2 Mean square error, epsilon represents the second loss value;
the step of training the initial parameters in the preset GPT model and generating the mileage calculation model includes:
generating a corresponding test data set according to the coding vector, and preprocessing the test data set;
dividing the test data set into a test set and a verification set according to a preset proportion, and inputting the test set into the preset GPT model to correspondingly train an initial mileage calculation model;
verifying the initial mileage calculation model through the verification set, and judging whether a verification result is qualified or not in real time;
if the verification result is judged to be qualified in real time, the verification of the initial mileage calculation model is completed, so that the mileage calculation model is correspondingly generated;
the step of inputting the test set into the preset GPT model to correspondingly train an initial mileage calculation model includes:
inputting the test set into an analysis layer of the preset GPT model, and analyzing the test set into corresponding training codes through the analysis layer, wherein the training codes consist of numbers and letters;
inputting the training codes into a training layer of the preset GPT model, and training original model parameters in the training layer through the training codes so as to adjust the original model parameters into target model parameters which are suitable for the vehicle;
inputting the target model parameters into a storage layer of the preset GPT model to correspondingly train out the initial mileage calculation model;
the step of verifying the initial mileage calculation model by the verification set includes:
iteratively inputting the verification set into the initial mileage calculation model so that the initial mileage calculation model correspondingly outputs a plurality of predicted values, and judging whether the difference values among the plurality of predicted values are within a preset threshold value one by one;
and if the difference value among the plurality of predicted values is judged to be within the preset threshold value in real time, setting the predicted value as the verification result.
2. The new energy electric automobile mileage calculation method according to claim 1, wherein: the method further comprises the steps of:
monitoring the change of the remaining range in real time, and judging whether the remaining range is larger than a preset range threshold in real time;
and if the residual continuous voyage mileage is judged to be smaller than the preset mileage threshold in real time, corresponding prompt information is sent out in an instrument panel of the vehicle, wherein the prompt information comprises a text prompt and an icon prompt.
3. The new energy electric automobile mileage calculation method according to claim 2, wherein: the method further comprises the steps of:
when the remaining endurance mileage is judged to be smaller than the preset mileage threshold value, detecting the running place of the current vehicle in real time, and searching a corresponding charging station around the running place;
and displaying the position of the charging station in the instrument panel of the vehicle in real time, and sending out a voice prompt.
4. A new energy electric automobile mileage calculation system, characterized in that it is used for implementing the new energy electric automobile mileage calculation method according to any one of claims 1-3, said system comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring a departure place and a destination of a vehicle driven by a user in real time and analyzing a commute route of the user according to the departure place and the destination;
the calculation module is used for acquiring weather information on the commute route in real time and calculating a first loss value of the vehicle on the commute route according to the weather information;
the output module is used for detecting a second loss value of the vehicle-mounted electrical appliance of the vehicle in real time through a preset sensor, and inputting the first loss value and the second loss value into a trained mileage calculation model at the same time, so that the mileage calculation model outputs the residual endurance mileage corresponding to the vehicle in real time;
the output module is specifically configured to:
acquiring performance parameters of the vehicle, and converting the performance parameters into corresponding performance vectors;
and carrying out coding processing on the performance vector so as to convert the performance vector into a corresponding coding vector, and inputting the coding vector into a preset GPT model so as to train initial parameters in the preset GPT model and generate the mileage calculation model.
5. A computer comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the computer program, implements the new energy electric vehicle mileage calculation method according to any one of claims 1-3.
6. A readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements the new energy electric vehicle mileage calculation method according to any one of claims 1 to 3.
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