CN115688957A - Vehicle energy consumption determination method and device, electronic equipment and storage medium - Google Patents
Vehicle energy consumption determination method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN115688957A CN115688957A CN202110868916.9A CN202110868916A CN115688957A CN 115688957 A CN115688957 A CN 115688957A CN 202110868916 A CN202110868916 A CN 202110868916A CN 115688957 A CN115688957 A CN 115688957A
- Authority
- CN
- China
- Prior art keywords
- data
- energy consumption
- historical
- current
- driving behavior
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Landscapes
- Navigation (AREA)
Abstract
The embodiment of the invention discloses a vehicle energy consumption determination method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring current energy consumption related data in a target route, wherein the current energy consumption related data comprises at least one of current road condition data, current weather data and current vehicle data; determining the current driving behavior prediction data of the user in the target route according to a pre-trained driving behavior prediction model; and inputting the current energy consumption related data and the current driving behavior prediction data into a pre-trained energy consumption prediction model, and determining the current vehicle energy consumption prediction data in the target route. By operating the technical scheme provided by the embodiment of the invention, the problem that the difference between the calculated endurance mileage and the actual endurance mileage in driving is often large can be solved, and the effect of improving the accuracy of determining the energy consumption of the vehicle is realized.
Description
Technical Field
The embodiment of the invention relates to vehicle technologies, in particular to a method and a device for determining vehicle energy consumption, an electronic device and a storage medium.
Background
At the present stage, a user of the new energy vehicle has mileage anxiety during driving, and pays more attention to the endurance mileage displayed by a vehicle instrument panel.
At present, the endurance mileage in a new energy vehicle is calculated by a calculation formula stored in an electronic control unit in the vehicle, and the difference between the endurance mileage displayed by a dashboard and the actual driving endurance mileage is large due to the fact that the input parameter data in the calculation formula are fixed simulation data, and the situation that no electricity is available and even road rescue needs to be started can occur.
Disclosure of Invention
The embodiment of the invention provides a vehicle energy consumption determination method and device, electronic equipment and a storage medium, and aims to improve the accuracy of vehicle energy consumption determination.
In a first aspect, an embodiment of the present invention provides a vehicle energy consumption determination method, including:
acquiring current energy consumption related data in a target route, wherein the current energy consumption related data comprises at least one of current road condition data, current weather data and current vehicle data;
determining the current driving behavior prediction data of the user in the target route according to a pre-trained driving behavior prediction model;
and inputting the current energy consumption related data and the current driving behavior prediction data into a pre-trained energy consumption prediction model, and determining the current vehicle energy consumption prediction data in the target route.
In a second aspect, an embodiment of the present invention further provides a vehicle energy consumption determination apparatus, including:
the system comprises a current energy consumption related data acquisition module, a data processing module and a data processing module, wherein the current energy consumption related data acquisition module is used for acquiring current energy consumption related data in a target route, and the current energy consumption related data comprises at least one of current road working condition data, current weather data and current vehicle data;
the current driving behavior prediction data determining module is used for determining the current driving behavior prediction data of the user in the target route according to a pre-trained driving behavior prediction model;
and the current vehicle energy consumption prediction data determining module is used for inputting the current energy consumption related data and the current driving behavior prediction data into a pre-trained energy consumption prediction model and determining the current vehicle energy consumption prediction data in the target route.
In a third aspect, an embodiment of the present invention further provides an electronic device, where the electronic device includes:
one or more processors;
a storage device to store one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the vehicle energy consumption determination method as described above.
In a fourth aspect, the embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the vehicle energy consumption determination method as described above.
According to the embodiment of the invention, the current energy consumption related data in the target route is acquired, wherein the current energy consumption related data comprises at least one of current road working condition data, current weather data and current vehicle data; determining the current driving behavior prediction data of the user in the target route according to a pre-trained driving behavior prediction model; and inputting the current energy consumption related data and the current driving behavior prediction data into a pre-trained energy consumption prediction model, and determining the current vehicle energy consumption prediction data in the target route. The problem that the difference between the calculated endurance mileage and the actual endurance mileage is large is solved, and the effect of improving the accuracy of determining the energy consumption of the vehicle is achieved.
Drawings
FIG. 1 is a flowchart of a method for determining energy consumption of a vehicle according to an embodiment of the present invention;
fig. 2 is a training flowchart of an energy consumption prediction model according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a vehicle energy consumption determining apparatus according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a flowchart of a vehicle energy consumption determination method according to an embodiment of the present invention, where this embodiment is applicable to a case of calculating energy consumption of a new energy vehicle, and the method may be executed by a vehicle energy consumption determination apparatus according to an embodiment of the present invention, where the apparatus may be implemented by software and/or hardware. Referring to fig. 1, the vehicle energy consumption determination method provided by the embodiment includes:
and step 110, obtaining current energy consumption related data in the target route, wherein the current energy consumption related data comprises at least one of current road condition data, current weather data and current vehicle data.
The target route is a route on which the vehicle is about to travel, and the target route can be predicted according to a current travel position of the vehicle and a historical driving track of the user, which is not limited in this embodiment.
The current energy consumption related data are data which may affect the vehicle energy consumption calculation at present, and different current energy consumption related data can be obtained according to the actual energy consumption calculation requirement, wherein the current energy consumption related data comprise at least one of current road working condition data, current weather data and current vehicle data.
The current road condition data is road condition data on the target route, such as gradient, curvature, link number and the like, and can be determined according to the road network data.
The current weather data is the weather conditions, such as temperature, wind, rain, snow, etc., of the area where the target route is located. For example, wind power may be obtained through a weather interface in the vehicle, and temperature may be obtained directly from weather data.
The current vehicle data is the condition data of the vehicle itself, such as the weight of the vehicle occupant, whether to turn on the air conditioner and the operating gear of the air conditioner, whether to heat the seat and the heating gear of the seat, and the like, and can be obtained according to the signal transmission of the corresponding electronic equipment in the vehicle. For example, the weight of the vehicle occupant may be determined based on the pressure signal on the seat, to facilitate subsequent estimation of the overall weight of the vehicle.
In this embodiment, optionally, before acquiring the current energy consumption related data in the target route, the method further includes:
determining the target route in response to a route planning activity of a user.
The user's route planning activities may include determining candidate routes to be traveled by the vehicle from the current location to the destination by obtaining user input to the destination, determining a target route from the candidate routes by user selection, and the like. The user may also send a path planning request through a navigation system of the vehicle, which is not limited in this embodiment.
According to the route planning behavior of the user, the target route is determined, the pertinence of route determination is improved, and the user experience is improved.
And step 120, determining the current driving behavior prediction data of the user in the target route according to a pre-trained driving behavior prediction model.
The driving behavior prediction model is used for predicting driving behaviors, such as rapid acceleration, rapid deceleration, overspeed and the like, of the user in the target route, calculating all the driving behaviors, and finally obtaining and outputting current driving behavior prediction data. The current driving behavior prediction data may be a prediction value of the driving behavior, and the value may represent the quality of the driving behavior of the user in the target route, such as the possibility of violation.
The required current driving behavior related data may be determined and obtained according to the target route, where the current driving behavior related data may include current road condition data, current weather data, and the like in the target route.
The current road condition data and the current weather data of the current driving behavior related data and the current energy consumption related data may be the same data, for example, the rain condition in the current weather data may be obtained similarly.
The road type of the route in the current road condition data, such as national road or provincial road, may also be obtained for different data, such as the current driving behavior-related data, and the same driving behavior may have different influences on the calculation of the current driving behavior prediction data in different roads, for example, the same rapid acceleration behavior, and the influence on the calculation of the current driving behavior prediction data in the expressway is larger than that on other types of roads. I.e. focusing on acquiring the corresponding data according to different data acquisition needs.
And step 130, inputting the current energy consumption related data and the current driving behavior prediction data into a pre-trained energy consumption prediction model, and determining the current vehicle energy consumption prediction data in the target route.
The data processing method can be used for processing the current energy consumption related data and the current driving behavior prediction data to obtain data of a format type required by the energy consumption prediction model, the data processing can be vectorization processing, the vectorization processing is not limited by the embodiment, the processed current driving behavior prediction data is obtained, direct subsequent calculation can be conveniently carried out, and the subsequent calculation efficiency is improved.
The current energy consumption related data and the current driving behavior prediction data are input to the energy consumption prediction model to obtain current vehicle energy consumption prediction data in the target route, where the current vehicle energy consumption prediction data may be battery power or power percentage that may be consumed by the vehicle when the vehicle travels in the target route, which is not limited in this embodiment.
In this embodiment, optionally, after determining the current vehicle energy consumption prediction data in the target route, the method further includes:
comparing the current vehicle energy consumption prediction data with the current vehicle battery data to obtain a second comparison result;
and determining whether to recommend a charging device according to the second comparison result.
Comparing the current vehicle energy consumption prediction data with the current vehicle battery data, and obtaining a second comparison result, which may be whether the target route driving condition is met, where, for example, the current vehicle energy consumption prediction data is that fifty percent of battery power is to be consumed, the current vehicle battery data includes the current remaining battery capacity, which is forty percent, and the target route driving condition is not met.
When the second comparison result is that the target route running condition is not satisfied or the target route running condition is satisfied but the remaining capacity of the target route is small, the charging device recommendation may be made. The recommended charging device may be located near the departure place or near the destination, which is not limited in this embodiment.
And determining whether to recommend a charging device or not based on a comparison result of the current vehicle energy consumption prediction data and the current vehicle battery data, so that the possibility of completing the target route driving of the vehicle is improved, and the user experience is improved.
According to the technical scheme provided by the embodiment, the current energy consumption related data in the target route are obtained, wherein the current energy consumption related data comprise at least one of current road working condition data, current weather data and current vehicle data; determining the current driving behavior prediction data of the user in the target route according to a pre-trained driving behavior prediction model; and inputting the current energy consumption related data and the current driving behavior prediction data into a pre-trained energy consumption prediction model, and determining the current vehicle energy consumption prediction data in the target route. The problem that the discharge capacity of the vehicle battery is greatly influenced by the discharge strength and the external environment due to the existence of special chemical characteristics is solved. However, before the vehicle starts, the in-vehicle electronic control unit does not consider the environment in the driving route to which the vehicle is about to face and the driving behavior of the user, so that the in-vehicle electronic control unit can only calculate a universal driving distance through the current electric quantity and display the universal driving distance to the vehicle owner, and the result has no large error under the standard condition. However, when the external situation is different from the standard situation, the calculated distance to be traveled and the actual distance to be traveled have a large error, so that the effect of improving the accuracy of determining the energy consumption of the vehicle is achieved.
Example two
Fig. 2 is a flowchart of training an energy consumption prediction model according to a second embodiment of the present invention. The technical scheme is supplementary explained aiming at the training process of the energy consumption prediction model trained in advance. Compared with the scheme, the scheme is specifically optimized in that the training process of the energy consumption prediction model comprises the following steps:
inputting historical driving behavior related data in the data to be trained into the driving behavior prediction model to obtain historical driving behavior prediction data;
inputting the historical driving behavior prediction data and historical energy consumption related data in the data to be trained into the energy consumption prediction model to obtain historical vehicle energy consumption prediction data;
comparing the historical vehicle energy consumption prediction data with the historical vehicle actual energy consumption data in the data to be trained to obtain a first comparison result;
and if the first comparison result meets a preset standard, determining that the energy consumption prediction model is trained completely.
Specifically, the training flow chart of the energy consumption prediction model is shown in fig. 2:
Each piece of data to be trained may be composed of different parts, each part represents different types of data, such as historical driving behavior related data, where the historical driving behavior related data may include historical vehicle data, historical road condition data in a target route, historical weather data, and the like, and may be obtained by performing statistical analysis on the historical data after the vehicle is driven.
The driving behavior prediction model may be a nonlinear model with a neural network architecture as a core, which is not limited in this embodiment.
And inputting the historical driving behavior related data in the data to be trained into the driving behavior prediction model to obtain historical driving behavior prediction data, wherein the historical driving behavior prediction data can be a prediction value of the historical driving behavior.
In this embodiment, optionally, before inputting the historical driving behavior related data in the data to be trained into the driving behavior prediction model to obtain the historical driving behavior prediction data, the method further includes:
acquiring original historical energy consumption related data and original historical driving behavior related data;
vectorizing the original historical energy consumption related data and the original historical driving behavior related data to obtain the historical energy consumption related data and the historical driving behavior related data;
and acquiring actual energy consumption data of a historical vehicle, and determining the data to be trained according to the actual energy consumption data of the historical vehicle, the historical energy consumption related data and the historical driving behavior related data.
The original historical energy consumption related data and the original historical driving behavior related data are data which are directly collected and are not subjected to data processing, such as the temperature of 25 ℃ and the like. The higher the accuracy of data acquisition is, the larger the quantity of data is, and the higher the accuracy of the energy consumption data obtained by model reasoning, prediction and calculation is.
And vectorizing the original historical energy consumption related data and the original historical driving behavior related data to obtain the historical energy consumption related data and the historical driving behavior related data. The vectorization processing is to process data into a form convenient for model processing, illustratively, weather data in historical energy consumption related data is converted into 0 in case of no rain, 1 in case of rain, 2 in case of medium rain and 3 in case of heavy rain; the vehicle data is converted to 0 by turning on the air conditioner, 1 by heating the seat, etc.
Optionally, if a sparse vector or a sparse matrix appears in the data during the vectorization process, the data may be densified by using an embedding technique.
The method comprises the steps of obtaining actual energy consumption data of a historical vehicle, wherein the actual energy consumption data of the historical vehicle is actual energy consumption in a historical travel of the vehicle, and splicing the actual energy consumption data of the historical vehicle, the historical energy consumption related data and the historical driving behavior related data to obtain a piece of data to be trained.
The method has the advantages that the vectorization processing is carried out on the acquired original data, the model processing efficiency is improved, the relevant vectorized data are spliced and the like to obtain the data to be trained, the relevance of the data in the data to be trained is improved, and the accuracy of the subsequent model training is improved.
Historical energy consumption related data in the data to be trained are data which can be directly input into the model after data processing, historical driving behavior prediction data and historical energy consumption related data are input into the energy consumption prediction model, historical vehicle energy consumption prediction data are obtained, and the historical vehicle energy consumption prediction data are energy consumption predicted in a historical journey of a vehicle.
And step 230, comparing the historical vehicle energy consumption prediction data with the historical vehicle actual energy consumption data in the data to be trained to obtain a first comparison result.
Each piece of data to be trained corresponds to one piece of historical vehicle actual energy consumption data, the historical vehicle actual energy consumption data is actual energy consumption in a historical travel of the vehicle, historical vehicle energy consumption prediction data obtained through calculation of the energy consumption prediction model is compared with the historical vehicle actual energy consumption data to obtain a first comparison result, the comparison result can be a difference value, and the embodiment does not limit the comparison result.
And 240, if the first comparison result meets a preset standard, determining that the energy consumption prediction model is completely trained.
And if the comparison result meets a preset standard, for example, the difference value is within a preset threshold range, determining that the training of the energy consumption prediction model is finished. If the standard is not met, the parameters in the training process can be adjusted, and the result meets the expected standard through repeated iterative training.
According to the embodiment of the invention, the historical vehicle energy consumption prediction data is compared with the corresponding historical vehicle actual energy consumption data until the historical vehicle energy consumption prediction data meets the preset standard, so that the effectiveness of vehicle energy consumption model training is improved.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a vehicle energy consumption determining apparatus according to a third embodiment of the present invention. The device can be realized in a hardware and/or software mode, can execute the vehicle energy consumption determination method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method. As shown in fig. 3, the apparatus includes:
a current energy consumption related data obtaining module 310, configured to obtain current energy consumption related data in a target route, where the current energy consumption related data includes at least one of current road condition data, current weather data, and current vehicle data;
a current driving behavior prediction data determination module 320, configured to determine, according to a pre-trained driving behavior prediction model, current driving behavior prediction data of a user in the target route;
a current vehicle energy consumption prediction data determining module 330, configured to input the current energy consumption related data and the current driving behavior prediction data into a pre-trained energy consumption prediction model, and determine current vehicle energy consumption prediction data in the target route.
The method comprises the steps of obtaining current energy consumption related data in a target route, wherein the current energy consumption related data comprise at least one of current road working condition data, current weather data and current vehicle data; determining the current driving behavior prediction data of the user in the target route according to a pre-trained driving behavior prediction model; and inputting the current energy consumption related data and the current driving behavior prediction data into a pre-trained energy consumption prediction model, and determining the current vehicle energy consumption prediction data in the target route. The problem that the difference between the calculated endurance mileage and the actual endurance mileage is large is solved, and the effect of improving the accuracy of determining the energy consumption of the vehicle is achieved.
On the basis of the above technical solutions, optionally, the model training module includes:
the historical driving behavior prediction data obtaining unit is used for inputting the historical driving behavior related data in the data to be trained into the driving behavior prediction model to obtain historical driving behavior prediction data;
the historical vehicle energy consumption prediction data obtaining unit is used for inputting the historical driving behavior prediction data and historical energy consumption related data in the data to be trained into the energy consumption prediction model to obtain historical vehicle energy consumption prediction data;
the data comparison unit is used for comparing the historical vehicle energy consumption prediction data with the historical vehicle actual energy consumption data in the data to be trained to obtain a first comparison result;
and the model training completion determining unit is used for determining that the energy consumption prediction model is trained completely if the first comparison result meets a preset standard.
On the basis of the above technical solutions, optionally, the apparatus further includes:
the data acquisition unit is used for acquiring original historical energy consumption related data and original historical driving behavior related data before the historical driving behavior prediction data acquisition unit;
the data processing unit is used for vectorizing the original historical energy consumption related data and the original historical driving behavior related data to obtain the historical energy consumption related data and the historical driving behavior related data;
and the to-be-trained data determining unit is used for acquiring the actual energy consumption data of the historical vehicle and determining the to-be-trained data according to the actual energy consumption data of the historical vehicle, the historical energy consumption related data and the historical driving behavior related data.
On the basis of the above technical solutions, optionally, the apparatus further includes:
and the target route determining module is used for responding to the route planning behavior of the user before the current energy consumption related data acquiring module and determining the target route.
On the basis of the above technical solutions, optionally, the apparatus further includes:
the data comparison module is used for comparing the current vehicle energy consumption prediction data with the current vehicle battery data after the current vehicle energy consumption prediction data determination module to obtain a second comparison result;
and the charging device recommendation determining module is used for determining whether to recommend a charging device according to the second comparison result.
Example four
Fig. 4 is a schematic structural diagram of an electronic apparatus according to a fourth embodiment of the present invention, as shown in fig. 4, the electronic apparatus includes a processor 40, a memory 41, an input device 42, and an output device 43; the number of the processors 40 in the electronic device may be one or more, and one processor 40 is taken as an example in fig. 4; the processor 40, the memory 41, the input device 42 and the output device 43 in the electronic apparatus may be connected by a bus or other means, and the bus connection is exemplified in fig. 4.
The memory 41 is used as a computer-readable storage medium for storing software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the vehicle energy consumption determination method in the embodiment of the present invention. The processor 40 executes various functional applications and data processing of the electronic device by executing software programs, instructions and modules stored in the memory 41, that is, implements the vehicle energy consumption determination method described above.
The memory 41 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the memory 41 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, memory 41 may further include memory located remotely from processor 40, which may be connected to the electronic device through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
EXAMPLE five
Embodiments of the present invention also provide a storage medium containing computer-executable instructions, which when executed by a computer processor, perform a method for determining vehicle energy consumption, the method comprising:
acquiring current energy consumption related data in a target route, wherein the current energy consumption related data comprises at least one of current road condition data, current weather data and current vehicle data;
determining the current driving behavior prediction data of the user in the target route according to a pre-trained driving behavior prediction model;
and inputting the current energy consumption related data and the current driving behavior prediction data into a pre-trained energy consumption prediction model, and determining the current vehicle energy consumption prediction data in the target route.
Of course, the storage medium provided by the embodiment of the present invention contains computer-executable instructions, and the computer-executable instructions are not limited to the operations of the method described above, and may also execute the relevant operations in the vehicle energy consumption determination method provided by any embodiment of the present invention.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the methods according to the embodiments of the present invention.
It should be noted that, in the embodiment of the vehicle energy consumption determining apparatus, the included units and modules are only divided according to the functional logic, but are not limited to the above division as long as the corresponding functions can be realized; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A vehicle energy consumption determination method, comprising:
acquiring current energy consumption related data in a target route, wherein the current energy consumption related data comprises at least one of current road condition data, current weather data and current vehicle data;
determining the current driving behavior prediction data of the user in the target route according to a pre-trained driving behavior prediction model;
and inputting the current energy consumption related data and the current driving behavior prediction data into a pre-trained energy consumption prediction model, and determining the current vehicle energy consumption prediction data in the target route.
2. The method of claim 1, wherein the training process of the energy consumption prediction model comprises:
inputting historical driving behavior related data in data to be trained into the driving behavior prediction model to obtain historical driving behavior prediction data;
inputting historical driving behavior prediction data and historical energy consumption related data in the data to be trained into the energy consumption prediction model to obtain historical vehicle energy consumption prediction data;
comparing the historical vehicle energy consumption prediction data with the historical vehicle actual energy consumption data in the data to be trained to obtain a first comparison result;
and if the first comparison result meets a preset standard, determining that the energy consumption prediction model is trained completely.
3. The method according to claim 2, wherein before inputting the historical driving behavior related data in the data to be trained into the driving behavior prediction model to obtain the historical driving behavior prediction data, the method further comprises:
acquiring original historical energy consumption related data and original historical driving behavior related data;
vectorizing the original historical energy consumption related data and the original historical driving behavior related data to obtain the historical energy consumption related data and the historical driving behavior related data;
obtaining actual energy consumption data of a historical vehicle, and determining the data to be trained according to the actual energy consumption data of the historical vehicle, the historical energy consumption related data and the historical driving behavior related data.
4. The method of claim 1, further comprising, prior to obtaining the current energy consumption related data in the target route:
determining the target route in response to a route planning activity of a user.
5. The method of claim 1, further comprising, after determining current vehicle energy consumption prediction data in the target route:
comparing the current vehicle energy consumption prediction data with the current vehicle battery data to obtain a second comparison result;
and determining whether to recommend a charging device according to the second comparison result.
6. A vehicle energy consumption determination apparatus, comprising:
the system comprises a current energy consumption related data acquisition module, a data processing module and a data processing module, wherein the current energy consumption related data acquisition module is used for acquiring current energy consumption related data in a target route, and the current energy consumption related data comprises at least one of current road working condition data, current weather data and current vehicle data;
the current driving behavior prediction data determining module is used for determining the current driving behavior prediction data of the user in the target route according to a pre-trained driving behavior prediction model;
and the current vehicle energy consumption prediction data determining module is used for inputting the current energy consumption related data and the current driving behavior prediction data into a pre-trained energy consumption prediction model and determining the current vehicle energy consumption prediction data in the target route.
7. The apparatus of claim 6, wherein the model training module comprises:
the historical driving behavior prediction data obtaining unit is used for inputting the historical driving behavior related data in the data to be trained into the driving behavior prediction model to obtain historical driving behavior prediction data;
the historical vehicle energy consumption prediction data obtaining unit is used for inputting the historical driving behavior prediction data and historical energy consumption related data in the data to be trained into the energy consumption prediction model to obtain historical vehicle energy consumption prediction data;
the data comparison unit is used for comparing the historical vehicle energy consumption prediction data with the historical vehicle actual energy consumption data in the data to be trained to obtain a first comparison result;
and the model training completion determining unit is used for determining that the energy consumption prediction model is trained completely if the first comparison result meets a preset standard.
8. The apparatus of claim 7, further comprising:
the data acquisition unit is used for acquiring original historical energy consumption related data and original historical driving behavior related data before the historical driving behavior prediction data acquisition unit;
the data processing unit is used for vectorizing the original historical energy consumption related data and the original historical driving behavior related data to obtain the historical energy consumption related data and the historical driving behavior related data;
and the to-be-trained data determining unit is used for acquiring the actual energy consumption data of the historical vehicle and determining the to-be-trained data according to the actual energy consumption data of the historical vehicle, the historical energy consumption related data and the historical driving behavior related data.
9. An electronic device, characterized in that the electronic device comprises:
one or more processors;
a storage device to store one or more programs,
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the vehicle energy consumption determination method as recited in any of claims 1-5.
10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the vehicle energy consumption determination method as defined in any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110868916.9A CN115688957A (en) | 2021-07-30 | 2021-07-30 | Vehicle energy consumption determination method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110868916.9A CN115688957A (en) | 2021-07-30 | 2021-07-30 | Vehicle energy consumption determination method and device, electronic equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115688957A true CN115688957A (en) | 2023-02-03 |
Family
ID=85059234
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110868916.9A Pending CN115688957A (en) | 2021-07-30 | 2021-07-30 | Vehicle energy consumption determination method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115688957A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116945907A (en) * | 2023-09-19 | 2023-10-27 | 江西五十铃汽车有限公司 | New energy electric automobile mileage calculation method and system |
-
2021
- 2021-07-30 CN CN202110868916.9A patent/CN115688957A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116945907A (en) * | 2023-09-19 | 2023-10-27 | 江西五十铃汽车有限公司 | New energy electric automobile mileage calculation method and system |
| CN116945907B (en) * | 2023-09-19 | 2024-01-26 | 江西五十铃汽车有限公司 | New energy electric automobile mileage calculation method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113642633B (en) | Method, device, equipment and medium for classifying driving scene data | |
| CN111806239B (en) | Electric vehicle endurance mileage measuring method, electronic device, and storage medium | |
| CN110288096B (en) | Prediction model training method, prediction model training device, prediction model prediction method, prediction model prediction device, electronic equipment and storage medium | |
| CN109741626B (en) | Parking situation prediction method, scheduling method and system for parking lot | |
| CN113895307B (en) | Method and device for determining remaining mileage, electric vehicle and medium | |
| CN112002124B (en) | Vehicle travel energy consumption prediction method and device | |
| CN111523932A (en) | Scoring method, device and system for network car booking service and storage medium | |
| CN113415288B (en) | Sectional type longitudinal vehicle speed planning method, device, equipment and storage medium | |
| CN104900061A (en) | Road section travel time monitoring method and device | |
| US20150073933A1 (en) | Vehicle powertrain selector | |
| CN108177528A (en) | Pure electric automobile continual mileage evaluation method, system, equipment and storage medium | |
| CN111397616A (en) | Candidate charging pile determination method, device, equipment and medium | |
| CN112838247A (en) | Fuel cell system power model prediction calculation method, device, medium and equipment | |
| CN113954870A (en) | Automatic driving vehicle behavior decision optimization system based on digital twin technology | |
| CN115817183A (en) | Method and device for predicting driving range of pure electric vehicle | |
| CN115688957A (en) | Vehicle energy consumption determination method and device, electronic equipment and storage medium | |
| CN115164922A (en) | Path planning method, system, equipment and storage medium | |
| CN114254521A (en) | Vehicle energy consumption simulation method, device, equipment and computer storage medium | |
| US20220082397A1 (en) | Station recommendation | |
| CN112896171B (en) | Control method, device and equipment of vehicle, vehicle and storage medium | |
| Cabani et al. | Enhancement of energy consumption estimation for electric vehicles by using machine learning | |
| CN113525385A (en) | Method and device for predicting vehicle travel energy consumption | |
| CN114407662B (en) | Residual mileage prediction method and device, electric automobile and storage medium | |
| CN115952932B (en) | Method for predicting power consumption and hydrogen consumption of vehicle based on driving route | |
| CN113428168B (en) | Control method, system and medium for automatically driving vehicle and automobile |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |